A Sparse PCA Approach to Clustering

T. Tony Cai; Linjun Zhang

A Sparse PCA Approach to Clustering

Methodology 2016-02-18 v1 Machine Learning

Authors: T. Tony Cai , Linjun Zhang

Abstract

We discuss a clustering method for Gaussian mixture model based on the sparse principal component analysis (SPCA) method and compare it with the IF-PCA method. We also discuss the dependent case where the covariance matrix $\Sigma$ is not necessarily diagonal.

Keywords

principal component analysis clustering

Cite

@article{arxiv.1602.05236,
  title  = {A Sparse PCA Approach to Clustering},
  author = {T. Tony Cai and Linjun Zhang},
  journal= {arXiv preprint arXiv:1602.05236},
  year   = {2016}
}

Comments

This paper is part of a discussion of the paper "Important feature PCA for high dimensional clustering" by Jiashun Jin and Wanjie Wang to appear in The Annals of Statistics

A Sparse PCA Approach to Clustering

Abstract

Keywords

Cite

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