Accession Number:

ADA568726

Title:

Local Principal Component Pursuit for Nonlinear Datasets

Descriptive Note:

Conference paper

Corporate Author:

LOS ALAMOS NATIONAL LAB NM

Report Date:

2012-05-01

Pagination or Media Count:

5.0

Abstract:

A robust version of Principal Component Analysis PCA can be constructed via a decomposition of a data matrix into low-rank and sparse components, the former representing a low-dimensional linear model of the data, and the latter representing sparse deviations from the low-dimensional subspace. This decomposition has been shown to be highly effective, but the underlying model is not appropriate when the data are not modeled well by a single low-dimensional subspace. We construct a new decomposition corresponding to a more general underlying model consisting of a union of low-dimensional subspaces, and demonstrate its performance on a video background removal problem.

Subject Categories:

  • Numerical Mathematics
  • Cybernetics
  • Photography
  • Miscellaneous Detection and Detectors

Distribution Statement:

APPROVED FOR PUBLIC RELEASE