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Cite Details

Brendt Wohlberg, Rick Chartrand and James Theiler, "Local Principal Component Pursuit for Nonlinear Datasets", in Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), (Kyoto, Japan), doi:10.1109/ICASSP.2012.6288776, pp. 3925--3928, Mar 2012

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 the performance on a video background removal problem.

BibTeX Entry

@inproceedings{wohlberg-2012-local,
author = {Brendt Wohlberg and Rick Chartrand and James Theiler},
title = {Local Principal Component Pursuit for Nonlinear Datasets},
year = {2012},
month = Mar,
urlpdf = {http://math.lanl.gov/~brendt/Publications/Docs/wohlberg-2012-local.pdf},
booktitle = {Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
address = {Kyoto, Japan},
doi = {10.1109/ICASSP.2012.6288776},
pages = {3925--3928}
}