In this book you will find the use of sparse
Principal Component Analysis (PCA) for representing
high dimensional data for classification. Sparse
transformation reduces the data
volume/dimensionality without loss of critical
information, so that it can be processed efficiently
and assimilated by a human. We obtained sparse
representation of high dimensional dataset using
Sparse Principal Component Analysis (SPCA) and
Direct formulation of Sparse Principal Component
Analysis (DSPCA). Later we performed classification
using k Nearest Neighbor (kNN) Method and compared
its result...