Robust PCA as Bilinear Decomposition With Outlier-Sparsity Regularization

Volume: 60, Issue: 10, Pages: 5176 - 5190
Published: Oct 1, 2012
Abstract
Principal component analysis (PCA) is widely used for dimensionality reduction, with well-documented merits in various applications involving high-dimensional data, including computer vision, preference measurement, and bioinformatics. In this context, the fresh look advocated here permeates benefits from variable selection and compressive sampling, to robustify PCA against outliers. A least-trimmed squares estimator of a low-rank bilinear...
Paper Details
Title
Robust PCA as Bilinear Decomposition With Outlier-Sparsity Regularization
Published Date
Oct 1, 2012
Volume
60
Issue
10
Pages
5176 - 5190
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