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14 May 2018 Conformity evaluation of data samples by L1-norm principal-component analysis
Ying Liu, Dimitris A. Pados
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Proceedings Volume 10658, Compressive Sensing VII: From Diverse Modalities to Big Data Analytics; 1065809 (2018) https://doi.org/10.1117/12.2311893
Event: SPIE Commercial + Scientific Sensing and Imaging, 2018, Orlando, FL, United States
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.
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Ying Liu and Dimitris A. Pados "Conformity evaluation of data samples by L1-norm principal-component analysis", Proc. SPIE 10658, Compressive Sensing VII: From Diverse Modalities to Big Data Analytics, 1065809 (14 May 2018); https://doi.org/10.1117/12.2311893
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Cited by 3 scholarly publications.
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KEYWORDS
Algorithm development
Principal component analysis
Glasses
Video surveillance
Soil science
Statistical analysis
Video
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