An iterative coordinate descent algorithm to compute sparse low-rank approximations
Machine Learning
2022-02-09 v1 Data Structures and Algorithms
Numerical Analysis
Signal Processing
Numerical Analysis
Abstract
In this paper, we describe a new algorithm to build a few sparse principal components from a given data matrix. Our approach does not explicitly create the covariance matrix of the data and can be viewed as an extension of the Kogbetliantz algorithm to build an approximate singular value decomposition for a few principal components. We show the performance of the proposed algorithm to recover sparse principal components on various datasets from the literature and perform dimensionality reduction for classification applications.
Cite
@article{arxiv.2107.14608,
title = {An iterative coordinate descent algorithm to compute sparse low-rank approximations},
author = {Cristian Rusu},
journal= {arXiv preprint arXiv:2107.14608},
year = {2022}
}