JSM 2015 Preliminary Program

Online Program Home
My Program

Abstract Details

Activity Number: 698
Type: Contributed
Date/Time: Thursday, August 13, 2015 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Learning and Data Mining
Abstract #317204
Title: Bagging PCA: a robust approach to principal component analysis
Author(s): Jiaxi Liang* and Shoja'eddin Chenouri and Christopher Small
Companies: and University of Waterloo and University of Waterloo
Keywords: Influence function ; dimension reduction ; robust ; subspace
Abstract:

Many popular dimensionality reduction methods are considered to be highly sensitive to outliers, and some robust procedures are proposed without a general and well-established criterion. Traditionally, the robustness study of a spectral dimension reduction method is through the influence function defined on the eigenvalues and eigenvectors of some real symmetric matrix. However, it is difficult to see the big picture from these vector-valued functions, and what we are essentially interested in is the whole low-dimensional subspace that spanned by the top eigenvectors. Moreover, considering the intrinsic dimensionality is to be estimated, the influence function should incorporate the intrinsic dimension estimation. In this talk, we will develop different types of influence function that defined on the distance between subspaces, possibly with different dimensionalites.


Authors who are presenting talks have a * after their name.

Back to the full JSM 2015 program





For program information, contact the JSM Registration Department or phone (888) 231-3473.

For Professional Development information, contact the Education Department.

The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.

2015 JSM Online Program Home