# Principal Component Analysis - Part II

This post is Part-II of a three part series post on PCA. Other parts of the series can be found at the links below.

In this post, we will first apply built in commands to obtain results and then show how the same results can be obtained without using built-in commands. Through this post our aim is not to advocate the use of non-built-in functions. Rather, in our opinion, it enhances understanding by knowing what happens under the hood when a built-in function is called. In actual applications, readers should always use built functions as they are robust(almost always) and tested for efficiency.

This post is written in R. Equivalent MATLAB code for the same can be obtained from this link.

We will use French food data form reference [2]. Refer to the paper to know about the original source of the data. We will apply different methods to this data and compare the result. As the dataset is pretty small, one way to load the data into R is to create a dataframe in R using the values in the paper. Another way is to first create a csv file and then read the file into R/MATLAB. We have used the later approach.

#Create a dataframe of food data
class = rep(c("Blue_collar", "White_collar", "Upper_class"), times = 4)
children = rep(c(2,3,4,5), each = 3)
bread = c(332, 293, 372, 406, 386, 438, 534, 460, 385, 655, 584, 515)
vegetables = c(428, 559, 767, 563, 608, 843, 660, 699, 789, 776, 995, 1097)
fruit = c(354, 388, 562, 341, 396, 689, 367, 484, 621, 423, 548, 887)
meat = c(1437, 1527, 1948, 1507, 1501, 2345, 1620, 1856, 2366, 1848, 2056, 2630)
poultry = c(526, 567, 927, 544, 558, 1148, 638, 762, 1149, 759, 893, 1167)
milk = c(247, 239, 235, 324, 319, 243, 414, 400, 304, 495, 518, 561)
wine = c(427, 258, 433, 407, 363, 341, 407, 416, 282, 486, 319, 284)
food = data.frame(class, children, bread, vegetables, fruit, meat, poultry, milk, wine, stringsAsFactors = F)
food

          class children bread vegetables fruit meat poultry milk wine
1   Blue_collar        2   332        428   354 1437     526  247  427
2  White_collar        2   293        559   388 1527     567  239  258
3   Upper_class        2   372        767   562 1948     927  235  433
4   Blue_collar        3   406        563   341 1507     544  324  407
5  White_collar        3   386        608   396 1501     558  319  363
6   Upper_class        3   438        843   689 2345    1148  243  341
7   Blue_collar        4   534        660   367 1620     638  414  407
8  White_collar        4   460        699   484 1856     762  400  416
9   Upper_class        4   385        789   621 2366    1149  304  282
10  Blue_collar        5   655        776   423 1848     759  495  486
11 White_collar        5   584        995   548 2056     893  518  319
12  Upper_class        5   515       1097   887 2630    1167  561  284

# Centerd data matrix
cent_food = scale(food[,3:9],scale = F)
# Scaled data matrix
scale_food = scale(food[,3:9],scale = T)


## Covariance PCA

### Using built-in function

# Using built-in function
pca_food_cov = prcomp(food[,3:9],scale = F)
# Loading scores (we have printed only four columns out of seven)
(round(pca_food_cov$rotation[,1:4],2))   PC1 PC2 PC3 PC4 bread 0.07 -0.58 -0.40 0.11 vegetables 0.33 -0.41 0.29 0.61 fruit 0.30 0.10 0.34 -0.40 meat 0.75 0.11 -0.07 -0.29 poultry 0.47 0.24 -0.38 0.33 milk 0.09 -0.63 0.23 -0.41 wine -0.06 -0.14 -0.66 -0.31  # Factor score (we have printed only four PCs out of seven)  We have printed only four columns of loading scores out of seven. (round(pca_food_cov$x[,1:4],2))

          PC1     PC2     PC3    PC4
[1,] -635.05  120.89  -21.14 -68.97
[2,] -488.56  142.33  132.37  34.91
[3,]  112.03  139.75  -61.86  44.19
[4,] -520.01  -12.05    2.85 -13.70
[5,] -485.94   -1.17   65.75  11.51
[6,]  588.17  188.44  -71.85  28.56
[7,] -333.95 -144.54  -34.94  10.07
[8,]  -57.51  -42.86  -26.26 -46.55
[9,]  571.32  206.76  -38.45   3.69
[10,]  -39.38 -264.47 -126.43 -12.74
[11,]  296.04 -235.92   58.84  87.43
[12,]  992.83  -97.15  121.13 -78.39


We have printed only four principal components out of seven.

# Variances using built-in function
(round(pca_food_cov$sdev^2,2))  [1] 274831.02 26415.99 6254.11 2299.90 2090.20 338.39 65.81  # Total variance (sum(round(pca_food_cov$sdev^2,2)))

[1] 312295.4


## Comparison of variance before and after transformation

# Total variance before transformation
sum(diag(cov(food[,3:9])))

[1] 312295.4

# Total variance after transformation
sum(diag(cov(pca_food_cov$x)))  [1] 312295.4  Another important observation is to see how variance of each variable before transformation changes into variance of principal components. Note that total variance in this process remains same as seen from above codes. # Variance along variables before transformation round(diag(cov(food[,3:9])),2)   bread vegetables fruit meat poultry milk wine 11480.61 35789.09 27255.45 156618.39 62280.52 13718.75 5152.63  Note that calculation of variance is unaffected by centering data matrix. So variance of original data matrix as well as centered data matrix is same. Check it for yourself. Now see how PCA transforms these variance. # Variance along principal compoennts round(diag(cov(pca_food_cov$x)),2)

      PC1       PC2       PC3       PC4       PC5       PC6       PC7
274831.02  26415.99   6254.11   2299.90   2090.20    338.39     65.81

# We can obtain the same result using built-in fucntion
round(pca_food_cov$sdev^2,2)  [1] 274831.02 26415.99 6254.11 2299.90 2090.20 338.39 65.81  ### Performing covariance PCA manually using SVD svd_food_cov = svd(cent_food) # Loading scores round(svd_food_cov$v[,1:4],2) # We have printed only four columns

      [,1]  [,2]  [,3]  [,4]
[1,]  0.07 -0.58 -0.40  0.11
[2,]  0.33 -0.41  0.29  0.61
[3,]  0.30  0.10  0.34 -0.40
[4,]  0.75  0.11 -0.07 -0.29
[5,]  0.47  0.24 -0.38  0.33
[6,]  0.09 -0.63  0.23 -0.41
[7,] -0.06 -0.14 -0.66 -0.31

# Factor scores
round((cent_food %*% svd_food_cov$v)[,1:4],2) # only 4 columns printed   [,1] [,2] [,3] [,4] [1,] -635.05 120.89 -21.14 -68.97 [2,] -488.56 142.33 132.37 34.91 [3,] 112.03 139.75 -61.86 44.19 [4,] -520.01 -12.05 2.85 -13.70 [5,] -485.94 -1.17 65.75 11.51 [6,] 588.17 188.44 -71.85 28.56 [7,] -333.95 -144.54 -34.94 10.07 [8,] -57.51 -42.86 -26.26 -46.55 [9,] 571.32 206.76 -38.45 3.69 [10,] -39.38 -264.47 -126.43 -12.74 [11,] 296.04 -235.92 58.84 87.43 [12,] 992.83 -97.15 121.13 -78.39  # Variance of principal components round(svd_food_cov$d^2/11,2)

[1] 274831.02  26415.99   6254.11   2299.90   2090.20    338.39     65.81


Our data matrix contains 12 data points. So to find variance of principal components we have to divide the square of the diagonal matrix by 11. To know the theory behind it, refer Part-I

This procedure is not recommended because forming a covariance matrix is computationally not efficient for large matrices if data matrix contains smaller entries. So doing eigen analysis on covariance matrix may give erroneous results. However, for our example we can use it to obtain results.

eigen_food_cov = eigen(cov(cent_food))
round(eigen_food_cov$vectors[,1:4],2)   [,1] [,2] [,3] [,4] [1,] -0.07 0.58 -0.40 -0.11 [2,] -0.33 0.41 0.29 -0.61 [3,] -0.30 -0.10 0.34 0.40 [4,] -0.75 -0.11 -0.07 0.29 [5,] -0.47 -0.24 -0.38 -0.33 [6,] -0.09 0.63 0.23 0.41 [7,] 0.06 0.14 -0.66 0.31  # Factor scores round((cent_food %*% eigen_food_cov$vectors)[,1:4],2)

         [,1]    [,2]    [,3]   [,4]
[1,]  635.05 -120.89  -21.14  68.97
[2,]  488.56 -142.33  132.37 -34.91
[3,] -112.03 -139.75  -61.86 -44.19
[4,]  520.01   12.05    2.85  13.70
[5,]  485.94    1.17   65.75 -11.51
[6,] -588.17 -188.44  -71.85 -28.56
[7,]  333.95  144.54  -34.94 -10.07
[8,]   57.51   42.86  -26.26  46.55
[9,] -571.32 -206.76  -38.45  -3.69
[10,]   39.38  264.47 -126.43  12.74
[11,] -296.04  235.92   58.84 -87.43
[12,] -992.83   97.15  121.13  78.39

# Variance along principal components
round(eigen_food_cov$values,2)  [1] 274831.02 26415.99 6254.11 2299.90 2090.20 338.39 65.81  Instead of using the ‘cov()’ command to find the covariance matrix manually and perform its eigen analysis. cov_matrix_manual_food = (1/11)*t(cent_food) %*% cent_food eigen_food_new = eigen(cov_matrix_manual_food) # Loading scores round(eigen_food_new$vectors[,1:4],2)

      [,1]  [,2]  [,3]  [,4]
[1,] -0.07  0.58 -0.40  0.11
[2,] -0.33  0.41  0.29  0.61
[3,] -0.30 -0.10  0.34 -0.40
[4,] -0.75 -0.11 -0.07 -0.29
[5,] -0.47 -0.24 -0.38  0.33
[6,] -0.09  0.63  0.23 -0.41
[7,]  0.06  0.14 -0.66 -0.31

# Variance along principal components
round(eigen_food_new$values,2)  [1] 274831.02 26415.99 6254.11 2299.90 2090.20 338.39 65.81  There are also different ways to find total variance of the data matrix. We will explore some of the options. # Total varaiance before transformation sum(diag(cov(cent_food)))  [1] 312295.4  Note that total variance is invariant to translations. So calculating the total variance on raw data will also give the same answer. Check it to convince yourself. ## Correlation PCA When PCA is performed on a scaled data matrix (each variable is centered as well as variance of each variable is one), it is called correlation PCA. Before discussing correlation PCA we will take some time to see different ways in which we can obtain correlation matrix. ### Different ways to obtain correlation matrix. # Using built-in command round(cor(food[,3:9]),2)[,1:4] # We have printed only four columns   bread vegetables fruit meat bread 1.00 0.59 0.20 0.32 vegetables 0.59 1.00 0.86 0.88 fruit 0.20 0.86 1.00 0.96 meat 0.32 0.88 0.96 1.00 poultry 0.25 0.83 0.93 0.98 milk 0.86 0.66 0.33 0.37 wine 0.30 -0.36 -0.49 -0.44  # manually round((1/11)*t(scale_food) %*% scale_food,2)[,1:4]   bread vegetables fruit meat bread 1.00 0.59 0.20 0.32 vegetables 0.59 1.00 0.86 0.88 fruit 0.20 0.86 1.00 0.96 meat 0.32 0.88 0.96 1.00 poultry 0.25 0.83 0.93 0.98 milk 0.86 0.66 0.33 0.37 wine 0.30 -0.36 -0.49 -0.44  ## Performing correlation PCA using built-in function pca_food_cor = prcomp(food[,3:9],scale = T) # Loading scores round(pca_food_cor$rotation[,1:4],2) # Printed only four

             PC1   PC2   PC3   PC4
vegetables  0.47 -0.10  0.06 -0.02
fruit       0.45  0.21 -0.15  0.55
meat        0.46  0.14 -0.21 -0.05
poultry     0.44  0.20 -0.36 -0.32
milk        0.28 -0.52  0.44  0.45
wine       -0.21 -0.48 -0.78  0.31

# Factor scores
round(pca_food_cor$x[,1:4],2)   PC1 PC2 PC3 PC4 [1,] -2.86 0.36 -0.40 0.36 [2,] -1.89 1.79 1.31 -0.16 [3,] -0.12 0.73 -1.42 0.20 [4,] -2.04 -0.32 0.11 0.10 [5,] -1.69 0.16 0.51 0.16 [6,] 1.69 1.35 -0.99 -0.43 [7,] -0.93 -1.37 0.28 -0.26 [8,] -0.25 -0.63 -0.27 0.29 [9,] 1.60 1.74 -0.10 -0.40 [10,] 0.22 -2.78 -0.57 -0.25 [11,] 1.95 -1.13 0.99 -0.32 [12,] 4.32 0.10 0.57 0.72  # Variances along principal componentes round(pca_food_cor$sdev^2,2)

[1] 4.33 1.83 0.63 0.13 0.06 0.02 0.00

# Sum of vairances
sum(pca_food_cor$sdev^2)  [1] 7  ## Comparison of variance before and after transformation # Total variance before transformation sum(diag(cov(scale_food)))  [1] 7  # Total variance after transformation sum(diag(cov(pca_food_cor$x)))

[1] 7


Another important observation is to see how variance of each variable before transformation changes into variance of principal components. Note that total variance in this process remains same as seen from above codes.

# Variance along variables before transformation
round(diag(cov(scale_food)),2)

     bread vegetables      fruit       meat    poultry       milk       wine
1          1          1          1          1          1          1


This is obvious as we have scaled the matrix. Now see how PCA transforms these variance.

# Variance along principal compoennts
round(diag(cov(pca_food_cor$x)),2)   PC1 PC2 PC3 PC4 PC5 PC6 PC7 4.33 1.83 0.63 0.13 0.06 0.02 0.00  # We can obtain the same result using built-in fucntion round(pca_food_cor$sdev^2,2)

[1] 4.33 1.83 0.63 0.13 0.06 0.02 0.00


### Performing correlation PCA manually using SVD

svd_food_cor = svd(scale_food)
round(svd_food_cor$v[,1:4],2)   [,1] [,2] [,3] [,4] [1,] 0.24 -0.62 0.01 -0.54 [2,] 0.47 -0.10 0.06 -0.02 [3,] 0.45 0.21 -0.15 0.55 [4,] 0.46 0.14 -0.21 -0.05 [5,] 0.44 0.20 -0.36 -0.32 [6,] 0.28 -0.52 0.44 0.45 [7,] -0.21 -0.48 -0.78 0.31  # Factor scores round((scale_food %*% svd_food_cor$v)[,1:4],2)

       [,1]  [,2]  [,3]  [,4]
[1,] -2.86  0.36 -0.40  0.36
[2,] -1.89  1.79  1.31 -0.16
[3,] -0.12  0.73 -1.42  0.20
[4,] -2.04 -0.32  0.11  0.10
[5,] -1.69  0.16  0.51  0.16
[6,]  1.69  1.35 -0.99 -0.43
[7,] -0.93 -1.37  0.28 -0.26
[8,] -0.25 -0.63 -0.27  0.29
[9,]  1.60  1.74 -0.10 -0.40
[10,]  0.22 -2.78 -0.57 -0.25
[11,]  1.95 -1.13  0.99 -0.32
[12,]  4.32  0.10  0.57  0.72

# Variance along each principcal component
round(svd_food_cor$d^2/11,2)  [1] 4.33 1.83 0.63 0.13 0.06 0.02 0.00  # Sum of variances sum(svd_food_cor$d^2/11)

[1] 7


Again we have to divide by 11 to get eigenvalues of correlation matrix. Check the formulation of correlation matrix using scaled data matrix to convince yourself.

eigen_food_cor = eigen(cor(food[,3:9]))
round(eigen_food_cor$vectors)   [,1] [,2] [,3] [,4] [,5] [,6] [,7] [1,] 0 1 0 -1 0 1 0 [2,] 0 0 0 0 1 0 0 [3,] 0 0 0 1 0 1 0 [4,] 0 0 0 0 0 0 -1 [5,] 0 0 0 0 0 0 1 [6,] 0 1 0 0 0 0 0 [7,] 0 0 -1 0 0 0 0  # Factor scores round((scale_food %*% eigen_food_cor$vectors)[,1:4],2)

       [,1]  [,2]  [,3]  [,4]
[1,]  2.86 -0.36 -0.40  0.36
[2,]  1.89 -1.79  1.31 -0.16
[3,]  0.12 -0.73 -1.42  0.20
[4,]  2.04  0.32  0.11  0.10
[5,]  1.69 -0.16  0.51  0.16
[6,] -1.69 -1.35 -0.99 -0.43
[7,]  0.93  1.37  0.28 -0.26
[8,]  0.25  0.63 -0.27  0.29
[9,] -1.60 -1.74 -0.10 -0.40
[10,] -0.22  2.78 -0.57 -0.25
[11,] -1.95  1.13  0.99 -0.32
[12,] -4.32 -0.10  0.57  0.72

# Variances along each principal component
round(eigen_food_cor\$values,2)

[1] 4.33 1.83 0.63 0.13 0.06 0.02 0.00


I hope this post would help clear some of the confusions that a beginner might have while encountering PCA for the first time. Please send me a note if you find any errors.

## References

1. I.T. Jolliffe, Principal component analysis, 2nd ed, Springer, New York,2002.
2. Abdi, H., & Williams, L. J. (2010). Principal component analysis. Wiley interdisciplinary reviews: computational statistics, 2(4), 433-459.
##### Biswajit Sahoo
###### Machine Learning Engineer

My research interests include machine learning, deep learning, signal processing and data-driven machinery condition monitoring.