Principal Component Analysis

Introduction to Principal Component Analysis

In data science, we generally have large datasets with multiple features to work on. If the computation of your models gets slow enough or your system is not powerful enough to perform such a huge computation, then you might end up looking for the alternatives. This is where PCA comes in to picture. With the help of Principal Component Analysis, you reduce the dimension of the dataset that contains many features or independent variables that are highly correlated with each other, while keeping the variation in the dataset up to a maximum extent. The new features that are created by combining the correlated features are termed as principal components. These principal components are the eigenvectors that are decomposed from a covariance matrix; hence they are orthogonal.

Why do we need PCA?

PCA is primarily used for dimensionality reduction in domains like facial recognition, computer vision, image compression and finding patterns in the field of finance, psychology, data mining, etc. PCA is used to extract the important information out of the dataset by combining the redundant features. These features are expressed in the form of new variables termed as principal components. Since the visualization of the features in the dataset is limited, we can also use PCA to reduce the dimensionality of the dataset to 2 or 3 principal components and then visualize to get a better insight into it.

How PCA works?

Following steps are involved in the working of PCA:

• Data Normalization
• Computing the Covariance Matrix
• Computing the Eigen Value and vectors from the Covariance Matrix
• Choose the first k eigenvectors where k is the required dimension.
• Transform the datapoints into k dimension

1. Data Normalization

It is important to perform data scaling before running PCA on the dataset. Because if we use data of if different scales, then we end up getting miss leading principle components. To do so, you need to perform mean normalization, and optionally you can also perform feature scaling

Suppose we have m dataset, i.e.: x1,x2,…..XM of dimension n.

Then compute the mean of each feature using the following equation:

Where:

• u_j: It means of the j^th feature
• m: Size of the dataset
• x⁽ⁱ⁾_j: j^th feature of data sample i

Once you have the mean of each feature then update each x⁽ⁱ⁾_j with x⁽ⁱ⁾_j – u_j.

Note that if the features are of a different scale, then you must use the following equation to normalize the data:

Where:

• x⁽ⁱ⁾_j: j^th feature of data sample i
• s_j: The difference between the max and min element of the j^th feature

2. Computing the Covariance Matrix

Assuming that the reader knows about covariance and variance, We will see what the Covariance matrix is represented.

Assuming that we have a dataset of 4 dimensions (a, b, c, d) and the variance is represented as Va, Vb, Vc, Vd for each dimension and Covariance is represented as Ca,b for dimension across a and b, Cb,c for dimension across b and c and so on. Then the Covariance matrix is given as:

The variance of the dataset is the diagonal element in the Covariance Matrix while the covariance of the dataset is the off-diagonal element.

If we have a dataset X of m*n dimension where m is the number of data points, and n is the number of dimensions then the covariance matrix sigma is given by:

3. Computing the Eigen Value and vectors from the Covariance Matrix

Now we need to decompose the covariance matrix to get the eigenvector and the value. This is done using a single vector decomposition. I won’t be going in the details of svd as it is out of scope for this article. Though it is important to note that there are functions in the popular programming languages like MATLAB, python to compute the svd. In the octave, you can get the eigenvalue and vector using the svd() function.

Where:

• Σ: Covariance matrix (sigma)
• U: Matrix containing the eigenvectors
• S: Diagonal matrix containing the eigenvalues.

4. Choose the first k eigenvectors where k is the required dimension

Once we have the matrix containing the eigenvectors we can select the first k columns of it. Where k Is the required dimension.

U_reduce is the matrix of the eigenvector that is needed to perform the data compression.

5. Transform the datapoints into k dimension

To transform the dataset X ^n*m from n dimension to k dimension by taking the transpose of the U_reduce matrix and then multiply it with the dataset.

where:

• z: Its new features
• As a result, you will get a dataset of dimension m*k from m*n.

Properties of Principal Component Analysis

Following are the list of properties that are PCA possess:

1. It transforms high dimensional data set into a low dimension before using it for training the model.
2. Principle components of PCA are the linear combinations of the original features, the eigenvector found from the covariance matrix satisfies the principle of least square.
3. It helps in determining the features that are needed to explain the covariation.

Advantages of Principal Component Analysis

PCA’s main advantage is its low sensitivity towards noise due to dimensionality reduction. Following are the other advantages of PCA:

1. It speeds up the learning algorithm.
2. Increases Efficiency due to lower dimensions.
3. It reduces the space required to store the data.
4. Data Visualization
5. Low sensitivity towards the noise.
6. Removes redundant variables

Conclusion

PCA method is a part of the unsupervised machine learning technique. These techniques are most suitable for images that have no class labels. Introduction to PCA and its working has been provided. And as mentioned above the advantages of the PCA have also been discussed in this article.

Recommended Articles

This is a guide to Principal Component Analysis. Here we discuss why do we need PCA, how does it work with appropriate steps involved along with advantages of Principal Components. You can also go through our other related articles to learn more–

1. Matlab Commands
2. Multidimensional Array in C
3. Statistical Analysis Softwares
4. Statistical Analysis Regression