Principle component analysis

Principle component analysis is a linear dimension reduction algorithm. In concept principle component analysis find the axis along which the data has maximal variance if it were projected. It does this by finding the Eigenvector and Eigenvalues of the Covariance matrix. It uses these eigenvectors as a new basis of the data’s feature space.

It performs dimension reduction by only picking the eigenvectors which have the highest eigenvalue.