In this article, the handwritten digits dataset (*mnist_train*) is going to be used to visualize and demonstrate how *Principal Component Analysis* can be used to represent the digits in the low dimensional feature space as a linear combination of the principal components as orthonormal basis vectors.

- Here only the digit 8 is considered for the analysis.
- The data contains 477 handwritten images of the digit 8 and each digit is a 28x28 image which is stored as a row of length 784.
- Below are the original images of 8 in the dataset.

- Next
*PCA*is done on the dataset and the variance explained by the first few principal components are shown below.

Percentage variance explained by the first few principal components

```
## [1] 57.87 63.76 66.76 69.38 71.32 72.95 74.32 75.55 76.69 77.79 78.87
## [12] 79.87 80.72 81.50 82.25 82.95 83.60 84.18 84.74 85.26 85.77 86.26
## [23] 86.73 87.16 87.55 87.92 88.29 88.64 88.97 89.31 89.62 89.92 90.20
## [34] 90.47 90.72 90.97 91.20 91.43 91.64 91.84 92.03 92.22 92.41 92.59
## [45] 92.76 92.93 93.08 93.24 93.39 93.54 93.69 93.83 93.97 94.10 94.22
## [56] 94.35 94.47 94.58 94.69 94.81 94.91 95.02 95.12 95.22 95.31 95.41
## [67] 95.50 95.59 95.67 95.76 95.84 95.92 96.00 96.08 96.15
```

- The Principal components (the
*eigenvectors*) are then visualized. Since they (particularly the dominant ones) look like 8 we can name this orthonormal basis vectors in the rotated space as*eigen digits*. They typically capture different features of the handwritten digit 88, depending on different writing styles. - The first 210
*eigen-digits*are visualized (stacked in 15 rows and 14 columns), in the column-major order, with the first eigenvector is the top left one, the second one being the next one below the first one in the same column and so on. As can be seen, the first few dominant eigenvectors clearly represents the digit 8 and hence the name.

- Next let’s just focus on one single data point (one particular handwritten digit image) and try to see how it can be represented in the eigen-digits space, as linear combination of the eigen-digits. The next figure shows the one we shall now try to represent in the feature space.

`## [1] "Weights for the first 20 basis vectors for the digit"`

```
## [1] -1905.408055 1355.563894 564.485519 -78.098594 747.401692
## [6] 116.314375 -293.701813 249.330045 158.920772 -258.179455
## [11] -659.294321 -322.247876 215.188713 -341.871715 4.276662
## [16] 279.882382 301.395575 123.411382 67.083178 -285.374166
```

- The below figures show how the digit image looks like when it’s represented as
*linear comibination*of*only*the first k eigen vectors (eigen digits) only, where

k=1,2,…,75 in the same column major order. - As expected, the
*approximate representation*of the digit looks like the actual representation shown above when it’s expressed as linear combination of first few eigenvectors with the weights computed. - The
*approximate representation*of the digit resembles the original digit as more and more basis vectors are included in its representation.

- The representation of the original digit in the eigen-space using first k eigenvectors (k=1,2,..,784) is shown in the following animation.

- The next figure shows how the error (computed as
*Frobenius norm*of the difference of the original and the approximated digit image, expressed as the linear combination of the first few eigenvectors in the PCA space). As expected again, the figure shows that the error decreases very fast.