CMStatistics 2015: Start Registration
View Submission - CMStatistics
B1384
Topic: Contributed on Statistical regularization Title: One-stage estimation of principal component regression with sparse regularization Authors:  Shuichi Kawano - The University of Electro-Communications (Japan) [presenting]
Hironori Fujisawa - The Institute of Statistical Mathematics (Japan)
Toyoyuki Takada - National Institute of Genetics (Japan)
Toshihiko Shiroishi - National Institute of Genetics (Japan)
Abstract: Principal component regression (PCR) is a two-stage procedure that selects some principal components and then constructs a regression model regarding them as new explanatory variables. Note that the principal components are obtained from only explanatory variables and not considered with the response variable. To address this problem, we propose the sparse principal component regression (SPCR) that is a one-stage procedure for PCR. SPCR enables us to adaptively obtain sparse principal component loadings that are related to the response variable and select the number of principal components simultaneously. SPCR can be obtained by the convex optimization problem for each parameter with the coordinate descent algorithm. Numerical results are presented to illustrate the effectiveness of SPCR.