Practical --1.pdf
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This document provides an introduction to principal component analysis (PCA) for feature extraction. It describes PCA as a method to project high-dimensional data into a lower-dimensional subspace while retaining the most important information. The key steps are outlined as calculating the mean of each column, centering the data, computing the covariance matrix, performing eigendecomposition of the covariance matrix to obtain eigenvalues and eigenvectors, and projecting the data onto the new subspace defined by the principal components with the highest eigenvalues. Implementing PCA using scikit-learn's PCA class is also briefly discussed.
Practical --1.pdf
- 1. Ashoka bairwa Page 1 CENTRAL UNIVERSITY OF HARYANA Department of Computer Science & Engineering under SOET MACHINE LEARNING LAB Submitted by Ashoka Roll No:- 191890 Submitted to Dr. Sangeeta Assistant Professor Central University of Haryana (SOET)
- 2. Ashoka bairwa Page 2 PRACTICAL-1 Aim: Introduction about Principal Component Analysis (PCA) feature extraction. Theory: Principal Component Analysis, or PCA for short, is a method for reducing the dimensionality of data. It can be thought of as a projection method where data with m-columns (features) is projected into a subspace with m or fewer columns, whilst retaining the essence of the original data. The PCA method can be described and implemented using the tools of linear algebra. Implementation: PCA is an operation applied to a dataset, represented by an n x m matrix A that results in a projection of A which we will call B. Let’s walk through the steps of this operation. The first step is to calculate the mean values of each column. Or Next, we need to center the values in each column by subtracting the mean column value. The next step is to calculate the covariance matrix of the centered matrix C. Correlation is a normalized measure of the amount and direction (positive or negative) that two columns change together. Covariance is a generalized and unnormalized version of correlation across multiple columns. A covariance matrix is a calculation of covariance of a given matrix with covariance scores for every column with every other column, including itself.
- 3. Ashoka bairwa Page 3 Finally, we calculate the eigen decomposition of the covariance matrix V. This results in a list of eigenvalues and a list of eigenvectors. The eigenvectors represent the directions or components for the reduced subspace of B, whereas the eigenvalues represent the magnitudes for the directions. For more on this topic, see the post: Gentle Introduction to Eigen decomposition, Eigenvalues, and Eigenvectors for Machine Learning The eigenvectors can be sorted by the eigenvalues in descending order to provide a ranking of the components or axes of the new subspace for A. If all eigenvalues have a similar value, then we know that the existing representation may already be reasonably compressed or dense and that the projection may offer little. If there are eigenvalues close to zero, they represent components or axes of B that may be discarded. A total of m or less components must be selected to comprise the chosen subspace. Ideally, we would select k eigenvectors, called principal components, that have the k largest eigenvalues. Other matrix decomposition methods can be used such as Singular-Value Decomposition, or SVD. As such, generally the values are referred to as singular values and the vectors of the subspace are referred to as principal components. Once chosen, data can be projected into the subspace via matrix multiplication. Where A is the original data that we wish to project, B^T is the transpose of the chosen principal components and P is the projection of A. This is called the covariance method for calculating the PCA, although there are alternative ways to to calculate it.
- 4. Ashoka bairwa Page 4 Manually Calculate Principal Component Analysis
- 5. Ashoka bairwa Page 5 Reusable Principal Component Analysis ➢ We can calculate a Principal Component Analysis on a dataset using the PCA() class in the scikit-learn library. The benefit of this approach is that once the projection is calculated, it can be applied to new data again and again quite easily. ➢ When creating the class, the number of components can be specified as a parameter. ➢ The class is first fit on a dataset by calling the fit() function, and then the original dataset or other data can be projected into a subspace with the chosen number of dimensions by calling the transform() function. ➢ Once fit, the eigenvalues and principal components can be accessed on the PCA class via the explained_variance_ and components_ attributes.