Sparse PCA

Sparse PCA

Principal component analysis(PCA) is a vector space transform used to reduce multidimensional data sets to lower dimensions for analysis. It finds linear combinations of variables( called "principal components") that correspond to directions of maximal variance in the data.

Sparse PCA [ cite journal
author = H. Zou and T. Hastie and R. Tibshirani
year = 2004
title = Sparse principal component analysis
journal = Technical report, statistics department, Stanford University, 2004
url = http://www-stat.stanford.edu/~hastie/Papers/sparsepc.pdf
] is a technique to find sets of sparse vectors spanning a low-dimensional space that explain most of the variance present in the data.

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