Recent results on sparse principle component analysis

T. Tony Cai, Zongming Ma, Yihong Wu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Principal component analysis (PCA) is one of the most commonly used statistical procedures for dimension reduction. This paper presents some recent results on the minimax estimation of principal subspaces in high dimensions. Under mild technical conditions, we characterize the minimax risk for estimating the principal subspace under the quadratic loss within absolute constant factors.

Original languageEnglish (US)
Title of host publication2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013
Pages181-183
Number of pages3
DOIs
StatePublished - Dec 1 2013
Event2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013 - Saint Martin, France
Duration: Dec 15 2013Dec 18 2013

Publication series

Name2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013

Other

Other2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013
CountryFrance
CitySaint Martin
Period12/15/1312/18/13

ASJC Scopus subject areas

  • Computer Science Applications

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