Introduction to Principal component analysis in Machine Learning
Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components. The number of principal components is less than or equal to the smaller number of original variables or the number of observations. This transformation is defined in such a way that the first principal component has the largest possible variance (that is, accounts for as much of the variability in the data as possible), and each succeeding component in turn has the highest variance possible under the constraint that it is orthogonal to preceding components.
Video Tutorial Principle Component Analysis – Dimensionality Reduction in Machine Learning
PCA Algorithm Video Tutorial
Computation of the principal component vectors (PCA algorithm)
The following is an outline of the procedure for performing a principal component analysis on a given data. The procedure is heavily dependent on mathematical concepts. A knowledge of these concepts is essential to carry out this procedure.
Summary: Principal component analysis in Machine Learning
This article discusses what is Principal component analysis in Machine Learning and the steps to get the principal components using PCA algorithm. If you like the material share it with your friends. Like the Facebook page for regular updates and the YouTube channel for video tutorials.