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Principal Component Analysis

 July 14  | 0 Comments

Implement from scratch and validate with the  sklearn framework

“Excess of Everything is Bad”

The above line is true in machine learning. When the data has too many dimensions, then it becomes a problem for pattern learning. Too much information is bad because of 2 reasons:

  1. High compute and execution time
  2. The risk of compromise in the quality of the model fit.

When the dimension of data is too high, we need to find a way to reduce it. But that reduction has to be done in such a way that we maintain the original pattern of the data.  The algorithm that we are going to discuss in this blog does this job. It is quite famous and widely used for a variety of tasks. It’s known as the Principal Component Analysis (PCA).

The main purpose of a principal component analysis is the analysis of data to identify and find patterns to reduce the dimensions of the dataset with a minimal loss of information.

PCA is used to transform a high-dimensional dataset into a smaller-dimensional subspace – into a new coordinate system. In the new coordinate system, the first axis corresponds to the first principal component, which is the component that explains the greatest amount of the variance in the data.

In simple words, a principal component analysis is a method of extracting important variables known as principal components from a large set of variables available in a data set. It captures as much information as possible from the original high dimensional data. It represents the original data in terms of its principal components in a new dimension space.

What are the Principal Components?

Principal components are the underlying structure in the data. They are the directions where there is the most variance, the directions where the data is most spread out. There are multiple principal components of a data – each representing the different variance of the data. They are arranged in a chronological order of variance. The first PC will capture the most variance i.e. the most information about the data, followed by the second, third and so on.

Mathematical Explanation:

Mathematically, the principal components are the eigenvectors of the symmetric correlation or covariance matrix of the original dataset. This means the matrix should be numeric and have standardized data. Eigenvectors of real symmetric matrices are orthogonal. The principal components (eigenvectors) correspond to the direction (in the original n-dimensional space) with the greatest variance in the data.

Each eigenvector has a corresponding eigenvalue. An eigenvalue is a scalar. Recall that an eigenvector corresponds to a direction. A corresponding eigenvalue is a number that indicates how much variance there is in the data along that eigenvector (or principal component). A larger eigenvalue means that that principal component explains a large amount of the variance in the data. A principal component with a very small eigenvalue does not do a good job of explaining the variance in the data.

Before doing PCA:

When performing PCA, it is typically a good idea to standardize the data first. Because PCA seeks to identify the principal components with the highest variance, if the data is not properly standardized, attributes with large values and large variances (in absolute terms) will end up dominating the first principal component when they should not. Standardizing the data gets each attribute onto more or less the same scale so that each attribute has an opportunity to contribute to the principal component analysis.


When should you use PCA?

It is often helpful to use a dimensionality-reduction technique such as PCA prior to performing machine learning because:

  • Reducing the dimensionality of the dataset reduces the size of the space on which k-nearest-neighbors (kNN) must calculate distance, which improves the performance of kNN.
  • If your learning algorithm is too slow because the input dimension is too high, then using PCA to speed it up can be a reasonable choice. This is probably the most common application of PCA.
  • Reducing the dimensionality of the dataset reduces the number of degrees of freedom of the hypothesis, which reduces the risk of overfitting.
  • Reducing the dimensionality via PCA can simplify the dataset, facilitating description, visualization, and insight.
  • Visualizing the data in lower dimension is much more intuitive than a higher dimension. PCA finds an important application in cases where the data of higher dimension needs a good visual representation.

Let’s try doing PCA on a randomly generated dataset. We will implement things from scratch. Then we will also use the implementation from sklearn.decomposition module.

Summarizing the PCA approach, listed below are the 6 general steps for performing a principal component analysis, which we will investigate in the following sections.

  1. Take the entire dataset
  2. Normalize columns of A so that each feature has zero mean
  3. Compute sample covariance matrix Σ=AT x A/(m−1)
  4. Perform eigen-decomposition of Σ using np.linalg.eig(Sigma)
  5. Compress by ordering k eigenvectors according to largest eigenvalues and compute Axk
  6. Reconstruct from the compressed version by computing Axk x k.T

Python Implementation with code:

1. Import necessary libraries

%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from scipy import stats

# use seaborn plotting style defaults
import seaborn as sns; sns.set()

2. Take the entire dataset

We will generate a random dataset on the fly.

A0 = (np.random.random(size=(2, 2)) @ np.random.normal(size=(2, 200))).T
(200, 2)

We have got 200 rows of 2-D vectors stored in a matrix.

Let’s visualize the generated data:

plt.plot(A0[:, 0], A0[:, 1], 'o')


3. Normalize columns of A0 so that each feature has zero mean

mu = np.mean(A0,axis=0)
A = A0 - mu

[-2.44249065e-17 -1.11022302e-18]

Does A have zero mean across rows? Yes, they are pretty close to zero(notice the e-17/18 at the end).

4. Compute sample covariance matrix Σ=AT x A/(m−1)

# 2. Compute sample covariance matrix Sigma = {A^TA}/{(m-1)}
m,n = A.shape
Sigma = (A.T @ A)/(m-1)

[[0.68217761 0.23093475]
 [0.23093475 0.09883179]]

5. Perform eigen-decomposition of Σ using np.linalg.eig(Sigma)

Decompose the covariance matrix into eigenvectors and eigenvalues.

l,X = np.linalg.eig(Sigma)

[0.7625315 0.0184779]
[[ 0.94446029 -0.32862557]
 [ 0.32862557  0.94446029]]

6. Compress by ordering k eigenvectors according to largest eigenvalues and compute Axk

# Compress by ordering k evectors according to largest evalues and compute AX_k
print("Compressed - 2D to 1D:")
Acomp = A @ X[:,:1] # first 2 evectors
print(Acomp[:5,:]) # first 5 observations

Compressed - 2D to 1D:
 [ 1.07121393]

We have successfully compressed the 2-D dataset into a 1-D data.

7. Reconstruct from the compressed version

We can reconstruct the data back by using inverse transformation mathematically represented by Axk x k.T

# 5. Reconstruct from the compressed version by computing A X_k X_k^T
print("Reconstructed version - 1D to 2D:")
Arec = A @ X[:,:1] @ X[:,:1].T # first 2 evectors
print(Arec[:5,:]+mu) # first 5 obs, adding mu to compare to original

Reconstructed version - 1D to 2D:
[[-0.60566999 -0.22648439]
 [ 1.0452307   0.34794757]
 [-0.65397264 -0.24329133]
 [-2.14785286 -0.76308793]
 [-0.56154772 -0.21113202]]

8. Validate the implementation with PCA from sklearn.decomposition

from  sklearn.decomposition import PCA

pca = PCA(n_components=1) # two components # run PCA, putting in raw version for fun

print("Principal components:")

print("Compressed - 4D to 2D:")
print(pca.transform(A0)[:5,:]) # first 5 obs

print("Reconstructed - 2D to 4D:")
print(pca.inverse_transform(pca.transform(A0))[:5,:]) # first 5 obs

Principal components:
[[-0.94446029 -0.32862557]]
Compressed - 2D to 1D:
[[ 0.67676923]
 [ 0.72791236]
 [ 2.30964136]
 [ 0.63005232]]
Reconstructed - 1D to 2D:
[[-0.60566999 -0.22648439]
 [ 1.0452307   0.34794757]
 [-0.65397264 -0.24329133]
 [-2.14785286 -0.76308793]
 [-0.56154772 -0.21113202]]

We can see the same set of compressed vectors and decompressed vectors.

Applications of PCA:

  • Compression
  • Visualization
  • Speeding up Machine Learning Algorithms
  • Reducing Noise from the data

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