Recent results on sparse principle component analysis

Author

Cai, Tony T. ; Zongming Ma ; Yihong Wu

Author_Institution

Dept. of Stat., Univ. of Pennsylvania, Philadelphia, PA, USA

fYear

2013

fDate

15-18 Dec. 2013

Firstpage

181

Lastpage

183

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.

Keywords

data reduction; minimax techniques; principal component analysis; risk management; PCA; Sparse Principle Component Analysis; dimension reduction; minimax risk; multivariate analysis; principal subspace minimax estimation; quadratic loss; statistical procedures; Conferences; Convergence; Covariance matrices; Eigenvalues and eigenfunctions; Estimation; Principal component analysis; Sociology;

fLanguage

English

Publisher

ieee

Conference_Titel

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

Conference_Location

St. Martin

Print_ISBN

978-1-4673-3144-9

Type

conf

DOI

10.1109/CAMSAP.2013.6714037

Filename

6714037

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2923482