## The algorithm known as PCA and my taxonomy of linear dimension reductions

In one of my previous articles, I explained the importance of reducing dimensions. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are the simplest types of dimension reduction algorithms. In upcoming articles of mine, you are going to see what these algorithms do. In conclusion, diagonalization, which I mentioned in the last article, is what these algorithms are all about, but still in this article I can mainly cover only PCA.

*This is the third article of my article series “Illustrative introductions on dimension reduction.”

### 1. My taxonomy on linear dimension reduction

*If you soon want to know  what the algorithm called “PCA” is, you should skip this section for now to avoid confusion.

Out of the two algorithms I mentioned, PCA is especially important and you would see the same or similar ideas in various fields such as signal processing, psychology, and structural mechanics. However in most cases, the word “PCA” refers to one certain algorithm of linear dimension reduction. Most articles or study materials only mention the “PCA,” and this article is also going to cover only the algorithm which most poeple call “PCA.” However I found that PCA is only one branch of linear dimension reduction algorithms.

*From now on all the terms “PCA” in this article means the algorithm known as PCA unless I clearly mention the generalized KL transform.

*This chart might be confusing to you. According to PRML, PCA and KL transform is identical. PCA has two formulations, maximum variance formulation and minimum error formulation, and they can give the same result. However according to a Japanese textbook, which is very precise about this topic, KL transform has two formulations, and what we call PCA is based on maximum variance formulation. I am still not sure about correct terminology, but in this article I am going to call the most general algorithm “generalized KL transform,” I mean the root of the chart above.

*Most materials just explain the most major PCA, but if you consider this generalized KL transform, I can introduce an intriguing classification algorithm called subspace method. This algorithm was invented in Japan, and this is not so popular in machine learning textbooks in general, but learning this method would give you better insight into the idea of multidimensional space in machine learning. In the future, I am planning to cover this topic in this article series.

### 2. PCA

When someones mention “PCA,” I am sure for the most part that means the algorithm I am going to explain in the rest of this article. The most intuitive and straightforward way to explain PCA is that, PCA (Principal Component Analysis) of two or three dimensional data is fitting an oval to two dimensional data or fitting an ellipsoid to three dimensional data. You can actually try to plot some random dots on a piece of paper, and draw an oval which fits the dots the best. Assume that you have these 2 or 3 dimensional data below, and please try to put an oval or an ellipsoid to the data.

I think this is nothing difficult, but I have a question: what was the logic behind your choice?

Some might have roughly drawn its outline. Formulas of  “the surface” of general ellipsoids can be explained in several ways, but in this article you only have to consider ellipsoids whose center is the origin point of the coordinate system. In PCA you virtually shift data so that the mean of the data comes to the origin point of the coordinate system. When is a certain type of matrix, the formula of a D-dimensional ellipsoid whose center is identical to the origin point is as follows: , where . As is always the case with formulas in data science, you can visualize such ellipsoids if you are talking about 1, 2, or 3 dimensional data like in the figure below, but in general D-dimensional space, it is theoretical/imaginary stuff on blackboards.

*In order to explain the conditions which the matrix has to hold, I need another article, so for now please just assume that the is a kind of magical matrix.

You might have seen equations of 2 or 3 dimensional ellipsoids in the following way: , where or , where . These are special cases of the equation , where . In this case the axes of ellipsoids the same as those of the coordinate system. Thus in this simple case, or .

I am going explain these equations in detail in the upcoming articles. But thre is one problem: how would you fit an ellipsoid when a data distribution does not look like an ellipsoid?

In fact we have to focus more on another feature of ellipsoids: all the axes of an ellipsoid are orthogonal. In conclusion the axes of the ellipsoids are more important in PCA, so I do want you to forget about the surface of ellipsoids for the time being. You might get confused if you also think about the surface of ellipsoid now. I am planning to cover this topic in the next article. I hope this article, combined with the last one and the next one, would help you have better insight into the ideas which frequently appear in data science or machine learning context.

### 3. Fitting orthogonal axes on data

*If you have no trouble reading the chapter 12.1 of PRML, you do not need to this section or maybe even this article, but I hope at least some charts or codes of mine would enhance your understanding on this topic.

*I must admit I wrote only the essence of PCA formulations. If that seems too abstract to you, you should just breifly read through this section and go to the next section with a more concrete example. If you are confused, there should be other good explanations on PCA on the internet, and you should also check them. But at least the visualization of PCA in the next section would be helpful.

As I implied above, all the axes of ellipsoids are orthogonal, and selecting the orthogonal axes which match data is what PCA is all about. And when you choose those orthogonal axes, it is ideal if the data look like an ellipsoid. Simply putting we want the data to “swell” along the axes.

Then let’s see how to let them “swell,” more mathematically. Assume that you have 2 dimensional data plotted on a coordinate system as below (The samples are plotted in purple). Intuitively, the data “swell” the most along the vector . Also  it is clear that is the only vector orthogonal to . We can expect that the new coordinate system expresses the data in a better way, and you you can get new coordinate points of the samples by projecting them on new axes as done with yellow lines below.

Next, let’s think about a case in 3 dimensional data. When you have 3 dimensional data in a coordinate system as below,  the data “swell” the most also along . And the data swells the second most along . The two axes, or vectors span the plain in purple. If you project all the samples on the plain, you will get 2 dimensional data at the right side. It is important that we did not consider the third axis. That implies you might be able to display the data well with only 2 dimensional sapce, which is spanned by the two axes .

Thus the problem is how to calculate such axis . We want the variance of data projected on to be the biggest. The coordinate of on the axis . The coordinate of a data point on the axis is calculated by projecting on . In data science context, such projection is synonym to taking an inner  product of and , that is calculating .

*Each element of is the coordinate of the data point in the original coordinate system. And the projected data on whose coordinates are 1-dimensional correspond to only one element of transformed data.

To calculate the variance of projected data on , we just have to calculate the mean of variances of 1-dimensional data projected on . Assume that is the mean of data in the original coordinate, then the deviation of on the axis is calculated as , as shown in the figure. Hence the variance, I mean the mean of the deviation on is , where is the total number of data points. After some deformations, you get the next equation , where . is known as a covariance matrix.

We are now interested in maximizing the variance of projected data on  , and for mathematical derivation we need some college level calculus, so if that is too much for you, you can skip reading this part till the next section.

We now want to calculate with which is its maximum value. General including are just coordinate axes after PCA, so we are just interested in their directions. Thus we can set one constraint . Introducing a Lagrange multiplier, we have only to optimize next problem: . In conclusion satisfies . If you have read my last article on eigenvectors, you wold soon realize that this is an equation for calculating eigenvectors, and that means is one of eigenvectors of the covariance matrix S. Given the equation of eigenvector the next equation holds . We have seen that is a the variance of data when projected on a vector , thus the eigenvalue is the biggest variance possible when the data are projected on a vector.

Just in the same way you can calculate the next biggest eigenvalue , and it it the second biggest variance possible, and in this case the date are projected on , which is orthogonal to . As well you can calculate orthogonal 3rd 4th …. Dth eigenvectors.

*To be exact I have to explain the cases where we can get such D orthogonal eigenvectors, but that is going to be long. I hope I can to that in the next article.

### 4. Practical three dimensional example of PCA

We have seen that PCA is sequentially choosing orthogonal axes along which data points swell the most. Also we have seen that it is equal to calculating eigenvalues of the covariance matrix of the data from the largest to smallest one. From now on let’s work on a practical example of data. Assume that we have 30 students’ scores of Japanese, math, and English tests as below.

* I think the subject “Japanese” is equivalent to “English” or “language art” in English speaking countries, and maybe “Deutsch” in Germany. This example and the explanation are largely based on a Japanese textbook named 「これなら分かる応用数学教室　最小二乗法からウェーブレットまで」. This is a famous textbook with cool and precise explanations on mathematics for engineering. Partly sharing this is one of purposes of this article.

At the right side of the figure below is plots of the scores with all the combinations of coordinate axes. In total 9 inverse graphs are symmetrically arranged in the figure, and it is easy to see that English & Japanese or English and math have relatively high correlation. The more two axes have linear correlations, the bigger the covariance between them is.

In the last article, I visualized the eigenvectors of a matrix , and in fact the matrix is just a constant multiplication of this covariance matrix. I think now you understand that PCA is calculating the orthogonal eigenvectors of covariance matrix of data, that is diagonalizing covariance matrix with orthonormal eigenvectors. Hence we can guess that covariance matrix enables a type of linear transformation of rotation and expansion and contraction of vectors. And data points swell along eigenvectors of such matrix.

Then why PCA is useful? In order to see that at first, for simplicity assume that denote Japanese, Math, English scores respectively. The mean of the data is , and the covariance matrix of data in the original coordinate system is . The eigenvalues of  are , and , and their corresponding unit eigenvectors are respectively.    is an orthonormal matrix, where . As I explained in the last article, you can diagonalize with : .

In order to see how PCA is useful, assume that .

Let’s take a brief look at what a linear transformation by means. Each element of denotes coordinate of the data point   in the original coordinate system (In this case the original coordinate system is composed of , and ). enables a rotation of a rigid body, which means the shape or arrangement of data will not change after the rotation, and enables a reverse rotation of the rigid body.

*Roughly putting, if you hold a bold object such as a metal ball and rotate your arm, that is a rotation of a rigid body, and your shoulder is the origin point. On the other hand, if you hold something soft like a marshmallow, it would be squashed in your hand, and that is not a not a rotation of a rigid body.

You can rotate with like , and is the coordinate of projected on the axis .

Let’s see this more visually. Assume that the data point   is a purple dot and its position is expressed in the original coordinate system spanned by black arrows . By multiplying with , the purple point is projected on the red axes respectively, and the product denotes the coordinate point of the purple point in the red coordinate system. is rotated this way, but for now I think it is better to think that the data are projected on new coordinate axes rather than the data themselves are rotating.

Now that we have seen what rotation by means, you should have clearer image on what means. denotes the coordinates of data projected on new axes , which are unit eigenvectors of . In the coordinate system spanned by the eigenvectors, the data distribute like below.

By multiplying from both sides of the equation above, we get , which means you can express deviations of the original data as linear combinations of the three factors , and . We expect that those three factors contain keys for understanding the original data more efficiently. If you concretely write down all the equations for the factors: , , and . If you examine the coefficients of the deviations , and , we can observe that almost equally reflects the deviation of the scores of all the subjects, thus we can say is a factor indicating one’s general academic level. When it comes to Japanese and Math scores are important, so we can guess that this factor indicates whether the student is at more of “scientific side” or “liberal art side.” In the same way relatively makes much of one’s English score,  so it should show one’s “internationality.” However the covariance of the data is . You can see does not vary from students to students, which means it is relatively not important to describe the tendency of data. Therefore for dimension reduction you can cut off the factor .

*Assume that you can apply PCA on D-dimensional data and that you get , where . The variance of data projected on new D-dimensional coordinate system is . This means that in the new coordinate system after PCA, covariances between any pair of variants are all zero.

*As I mentioned is a rotation of a rigid body, and is the reverse rotation, hence .

Hence you can approximate the original 3 dimensional data on the coordinate system from the reduced two dimensional coordinate system with the following equation: . Then it mathematically clearer that we can express the data with two factors: “how smart the student is” and “whether he is at scientific side or liberal art side.”

We can observe that eigenvalue is a statistic which indicates how much the corresponding can express the data, is called the contribution ratio of eigenvector . In the example above, the contribution ratios of and are respectively , , . You can decide how many degrees of dimensions you reduce based on this information.

### Appendix: Playing with my toy PCA on MNIST dataset

Applying “so called” PCA on MNIST dataset is a super typical topic that many other tutorial on PCA also introduce, but I still recommend you to actually implement, or at least trace PCA implementation with MNIST dataset without using libraries like scikit-learn. While reading this article I recommend you to actually run the first and the second code below. I think you can just copy and paste them on your tool to run Python, installing necessary libraries. I wrote them on Jupyter Notebook.

In my implementation, in the simple configuration part you can set the USE_ALL_NUMBERS as True or False boolean. If you set it as True, you apply PCA on all the data of numbers from 0 to 9. If you set it as True, you can specify which digit to apply PCA on. In this article, I show the results results of PCA on the data of digit ‘3.’ The first three images of ‘3’ are as below.

You have to keep it in mind that the data are all shown as 28 by 28 pixel grayscale images, but in the process of PCA, they are all processed as 28 * 28 = 784 dimensional vectors. After applying PCA on the 784 dimensional vectors of images of ‘3,’ the first 25 eigenvectors are as below. You can see that at the beginning the eigenvectors partly retain the shapes of ‘3,’ but they are distorted as the eigenvalues get smaller. We can guess that the latter eigenvalues are not that helpful in reconstructing the shape of ‘3.’

Just as we saw in the last section, you you can cut off axes of eigenvectors with small eigenvalues and reduce the dimension of MNIST data. The figure below shows how contribution ratio of MNIST data grows. You can see that around 200 dimension degree, the contribution ratio reaches around 0.95. Then we can guess that even if we reduce the dimension of MNIST from 784 to 200 we can retain the most of the structure of original data.

Some results of reconstruction of data from 200 dimensional space are as below. You can set how many images to display by adjusting NUMBER_OF_RESULTS in the code. And if you set LATENT_DIMENSION as 784, you can completely reconstruct the data.

* I make study materials on machine learning, sponsored by DATANOMIQ. I do my best to make my content as straightforward but as precise as possible. I include all of my reference sources. If you notice any mistakes in my materials, including grammatical errors, please let me know (email: yasuto.tamura@datanomiq.de). And if you have any advice for making my materials more understandable to learners, I would appreciate hearing it.

*I attatched the codes I used to make the figures in this article. You can just copy, paste, and run, sometimes installing necessary libraries.

## Rethinking linear algebra: visualizing linear transformations and eigenvectors

In terms of calculation processes of Principal Component Analysis (PCA) or Linear Discriminant Analysis (LDA), which are the dimension reduction techniques I am going to explain in the following articles, diagonalization is what they are all about. Throughout this article, I would like you to have richer insight into diagonalization in order to prepare for understanding those basic dimension reduction techniques.

When our professor started a lecture on the last chapter of our textbook on linear algebra, he said “It is no exaggeration to say that everything we have studied is for this ‘diagonalization.'” Until then we had to write tons of numerical matrices and vectors all over our notebooks, calculating those products, adding their rows or columns to other rows or columns, sometimes transposing the matrices, calculating their determinants.

It was like the scene in “The Karate Kid,” where the protagonist finally understood the profound meaning behind the prolonged and boring “wax on, wax off” training given by Miyagi (or “jacket on, jacket off” training given by Jackie Chan). We had finally understood why we had been doing those seemingly endless calculations.

But usually you can do those calculations easily with functions in the Numpy library. Unlike Japanese college freshmen, I bet you are too busy to reopen textbooks on linear algebra to refresh your mathematics. Thus I am going to provide less mathematical and more intuitive explanation of diagonalization in this article.

*This is the second article of the article series ” Illustrative introductions on dimension reduction .”

### 1, The mainstream ways of explaining diagonalization.

*The statements below are very rough for mathematical topics, but I am going to give priority to offering more visual understanding on linear algebra in this article. For further understanding, please refer to textbooks on linear algebra. If you would like to have minimum understandings on linear algebra needed for machine learning, I recommend the Appendix C of Pattern Recognition and Machine Learning by C. M. Bishop.

In most textbooks on linear algebra, the explanations on dioagonalization is like this (if you are not sure what diagonalization is or if you are allergic to mathematics, you do not have to read this seriously):

Let be a vector space and let  be a mapping of into itself,  defined as , where is a matrix and is dimensional vector. An element is called an eigen vector if there exists a number such that and . In this case is uniquely determined and is called an eigen value of belonging to the eigen vector .

Any matrix has eigen values , belonging to . If is basis of the vector space , then is diagonalizable.

When is diagonalizable, with matrices , whose column vectors are eigen vectors , the following equation holds: , where .

And when is diagonalizable, you can diagonalize as below.

Most textbooks keep explaining these type of stuff, but I have to say they lack efforts to make it understandable to readers with low mathematical literacy like me. Especially if you have to apply the idea to data science field, I believe you need more visual understanding of diagonalization. Therefore instead of just explaining the definitions and theorems, I would like to take a different approach. But in order to understand them in more intuitive ways, we first have to rethink waht linear transformation means in more visible ways.

### 2, Linear transformations

Even though I did my best to make this article understandable to people with little prerequisite knowledge, you at least have to understand linear transformation of numerical vectors and with matrices. Linear transformation is nothing difficult, and in this article I am going to use only 2 or 3 dimensional numerical vectors or square matrices. You can calculate linear transformation of by as equations in the figure. In other words, is a vector transformed by .

*I am not going to use the term “linear transformation” in a precise way in the context of linear algebra. In this article or in the context of data science or machine learning, “linear transformation” for the most part means products of matrices or vectors.

*Forward/back propagation of deep learning is mainly composed of this linear transformation. You keep linearly transforming input vectors, frequently transforming them with activation functions, which are for the most part not linear transformation.

As you can see in the equations above, linear transformation with transforms a vector to another vector. Assume that you have an original vector in grey and that the vector in pink is the transformed by is. If you subtract from , you can get a displacement vector, which I displayed in purple. A displacement vector means the transition from a vector to another vector.

Let’s calculate the displacement vector with more vectors . Assume that , and I prepared several grid vectors in grey as you can see in the figure below. If you transform those grey grid points with , they are mapped into the vectors in pink. With those vectors in grey or pink, you can calculate the their displacement vectors , which are in purple.

The displacement vectors in the figure above have some tendencies. In order to see that more clearly, let’s calculate displacement vectors with several matrices and more grid points. Assume that you have three square matrices , and I plotted displace vectors made by the matrices respectively in the figure below.

I think you noticed some characteristics of the displacement vectors made by those linear transformations: the vectors are swirling and many of them seem to be oriented in certain directions. To be exact, some displacement vectors extend in the same directions as some of original vectors in grey. That means  linear transformation by did not change the direction of the original vector , and the unchanged vectors are called eigen vectors. Real eigen vectors of each A are displayed as arrows in yellow in the figure above. But when it comes to , the matrix does not have any real eigan values.

In linear algebra, depending on the type matrices , you have to consider various cases such as whether the matrices have real or imaginary eigen values, whether the matrices are diagonalizable, whether the eigen vectors are orthogonal, or whether they are unit vectors. But those topics are out of the scope of this article series, so please refer to textbooks on linear algebra if you are interested.

Luckily, however, in terms of PCA or LDA, you only have to consider a type of matrices named positive semidefinite matrices, which is classified to, and I am going to explain positive semidefinite matrices in the fourth section.

### 3, Eigen vectors as coordinate system

Source: Ian Stewart, “Professor Stewart’s Cabinet of Mathematical Curiosities,” (2008), Basic Books

Let me take Fibonacci numbers as an example to briefly see why diagonalization is useful. Fibonacci is sequence is quite simple and it is often explained using an example of pairs of rabbits increasing generation by generation. Let be the number of pairs of grown up rabbits in the generation. One pair of grown up rabbits produce one pair of young rabbit The concrete values of are , , , , , , , . Assume that and that , then you can calculate the number of the pairs of grown up rabbits in the next generation with the following recurrence relation. .Let be , then the recurrence relation can be written as , and the transition of are like purple arrows in the figure below. It seems that the changes of the purple arrows are irregular if you look at the plots in normal coordinate.

Assume that are eigen values of , and are eigen vectors belonging to them respectively. Also let scalars such that . According to the definition of eigen values and eigen vectors belonging to them, the following two equations hold: . If you calculate is, using eigen vectors of , . In the same way, , and . These equations show that in coordinate system made by eigen vectors of , linear transformation by is easily done by just multiplying eigen values with each eigen vector. Compared to the graph of Fibonacci numbers above, in the figure below you can see that in coordinate system made by eigen vectors the plots changes more systematically generation by generation.

In coordinate system made by eigen vectors of square matrices, the linear transformations by the matrices can be much more straightforward, and this is one powerful strength of eigen vectors.

*I do not major in mathematics, so I am not 100% sure, but vectors in linear algebra have more abstract meanings. Various things in mathematics can be vectors, even though in machine learning or data science we  mainly use numerical vectors with more concrete elements. We can also say that matrices are a kind of maps. That is just like, at least in my impression, even though a real town is composed of various components such as houses, smooth or bumpy roads, you can simplify its structure with simple orthogonal lines, like the map of Manhattan. But if you know what the town actually looks like, you do not have to follow the zigzag path on the map.

### 4, Eigen vectors of positive semidefinite matrices

In the second section of this article I told you that, even though you have to consider various elements when you discuss general diagonalization, in terms of PCA and LDA we mainly use only a type of matrices named positive semidefinite matrices. Let be a square matrix. If for all values of the vector , the is said to be a positive semidefinite matrix. And also it is known that being a semidefinite matrix is equivalent to for all the eigen values .

*I think most people first learn a type of matrices called positive definite matrices. Let be a square matrix. If for all values of the vector , the is said to be a positive definite matrix. You have to keep it in mind that even if all the elements of are positive, is not necessarly positive definite/semidefinite.

Just as we did in the second section of this article, let’s visualize displacement vectors made by linear transformation with a square positive semidefinite matrix .

*In fact , whose linear transformation I visualized the second section, is also positive semidefinite.

Let’s visualize linear transformations by a positive definite matrix . I visualized the displacement vectors made by the just as the same way as in the second section of this article. The result is as below, and you can see that, as well as the displacement vectors made by , the three dimensional displacement vectors below are swirling and extending in three directions, in the directions of the three orthogonal eigen vectors , and .

*It might seem like a weird choice of a matrix, but you are going to see why I chose it in the next article.

You might have already noticed and are both symmetric matrices and that their elements are all real values, and that their diagonal elements are all positive values. Super importantly, when all the elements of a symmetric matrix are real values and its eigen values are , there exist orthonormal matrices such that , where .

*The title of this section might be misleading, but please keep it in mind that positive definite/semidefinite matrices are not necessarily real symmetric matrices. And real symmetric vectors are not necessarily positive definite/semidefinite matrices.

### 5, Orthonormal matrices and rotation of vectors

In this section I am gong to explain orthonormal matrices, as known as rotation matrices. If a matrix is an orthonormal matrix, column vectors of are orthonormal, which means , where . In other words column vectors form an orthonormal coordinate system.

Orthonormal matrices have several important properties, and one of the most important properties is . Combining this fact with what I have told you so far, you we can reach one conclusion: you can orthogonalize a real symmetric matrix as . This is known as spectral decomposition or singular value decomposition.

Another important property of is that is also orthonormal. In other words, assume is orthonormal and that , also forms a orthonormal coordinate system.

…It seems things are getting too mathematical and abstract (for me), thus for now I am going to wrap up what I have explained in this article .

We have seen

• Numerical matrices linearly transform vectors.
• Certain linear transformations do not change the direction of vectors in certain directions, which are called eigen vectors.
• Making use of eigen vectors, you can form new coordinate system which can describe the linear transformations in a more straightforward way.
• You can diagonalize a real symmetric matrix with an orthonormal matrix .

Of our current interest is what kind of linear transformation the real symmetric positive definite matrix enables. I am going to explain why the purple vectors in the figure above is swirling in the upcoming articles. Before that, however, we are going to  see one application of what we have seen in this article, on dimension reduction. To be concrete the next article is going to be about principal component analysis (PCA), which is very important in many fields.

*In short, the orthonormal matrix , which I mentioned above enables rotation of matrix, and the diagonal matrix expands or contracts vectors along each axis. I am going to explain that more precisely in the upcoming articles.

* I make study materials on machine learning, sponsored by DATANOMIQ. I do my best to make my content as straightforward but as precise as possible. I include all of my reference sources. If you notice any mistakes in my materials, including grammatical errors, please let me know (email: yasuto.tamura@datanomiq.de). And if you have any advice for making my materials more understandable to learners, I would appreciate hearing it.

*I attatched the codes I used to make the figures in this article. You can just copy, paste, and run, sometimes installing necessary libraries.

## Illustrative introductions on dimension reduction

“What is your image on dimensions?”

….That might be a cheesy question to ask to reader of Data Science Blog, but most people, with no scientific background, would answer “One dimension is a line, and two dimension is a plain, and we live in three-dimensional world.” After that if you ask “How about the fourth dimension?” many people would answer “Time?”

You can find books or writings about dimensions in various field. And you can use the word “dimension” in normal conversations, in many contexts.

*In Japanese, if you say “He likes two dimension.” that means he prefers anime characters to real women, as is often the case with Japanese computer science students.

The meanings of “dimensions” depend on the context, but in data science dimension is usually the number of rows of your Excel data.

When you study data science or machine learning, usually you should start with understanding the algorithms with 2 or 3 dimensional data, and you can apply those ideas to any D dimensional data. But of course you cannot visualize D dimensional data anymore, and you always have to be careful of what happens if you expand degree of dimension.

Conversely it is also important to reduce dimension to understand abstract high dimensional stuff in 2 or 3 dimensional space, which are close to our everyday sense. That means dimension reduction is one powerful way of data visualization.

In this blog series I am going to explain meanings of dimension itself in machine learning context and algorithms for dimension reductions, such as PCA, LDA, and t-SNE, with 2 or 3 dimensional visible data. Along with that, I am going to delve into the meaning of calculations so that you can understand them in more like everyday-life sense.

#### This article series is going to be roughly divided into the contents below.

1. Curse of Dimensionality
2. Rethinking linear algebra: visualizing linear transformations and eigen vector
3. The algorithm known as PCA and my taxonomy of linear dimension reductions
4. Rethinking linear algebra part two: ellipsoids in data science
5. Autoencoder as dimension reduction (to be published soon)
6. t-SNE (to be published soon)

I hope you could see that reducing dimension is one of the fundamental approaches in data science or machine learning.