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Conformity evaluation of data samples by L1-norm principal-component analysis
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Paper Abstract

We describe an iterative procedure for soft characterization of outlier data in any given data set. In each iteration, data compliance to nominal data behavior is measured according to current L1-norm principal-component subspace representations of the data set. Successively refined L1-norm subspace data set representations lead to successively refined outlier data characterization. The effectiveness of the proposed theoretical scheme is experimentally studied and the results show significantly improved performance compared to L2-PCA schemes, standard L1-PCA, and state-of-the-art robust PCA methods.

Paper Details

Date Published: 14 May 2018
PDF: 9 pages
Proc. SPIE 10658, Compressive Sensing VII: From Diverse Modalities to Big Data Analytics, 1065809 (14 May 2018); doi: 10.1117/12.2311893
Show Author Affiliations
Ying Liu, Univ. at Buffalo (United States)
Dimitris A. Pados, Florida Atlantic Univ. (United States)

Published in SPIE Proceedings Vol. 10658:
Compressive Sensing VII: From Diverse Modalities to Big Data Analytics
Fauzia Ahmad, Editor(s)

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