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Proceedings Paper

Conformity evaluation and L1-norm principal-component analysis of tensor data
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Paper Abstract

Multi-modal tensor data sets arise with increasing frequency in modern day scientific and engineering applications, for example in biomedical sciences and autonomous engineered systems. Over the past twenty years, tensor-domain data analysis has been attempted primarily in the context of standard (L2-norm) eigenvector decompositions across tensor domains. The algorithms are not joint-tensor-domain optimal and exhibit the familiar sensitivity to faulty/corrupted/missing measurements that characterizes all L2-norm principal-component analysis methods. In this work, we present a robustified method to evaluate the conformity of tensor data entries with respect to the whole accessible data set. Conformity evaluation is based on a continuously refined sequence of calculated L1- norm tensor subspaces. The theoretical developments are illustrated in the context of a multisensor localization application that indicates unprecedented estimation performance and resistance to intermittent disturbances. An electroencephalogram (EEG) data analysis experiment is also presented.

Paper Details

Date Published: 13 May 2019
PDF: 11 pages
Proc. SPIE 10989, Big Data: Learning, Analytics, and Applications, 109890P (13 May 2019); doi: 10.1117/12.2520538
Show Author Affiliations
Konstantinos Tountas, Florida Atlantic Univ. (United States)
Dimitris A. Pados, Florida Atlantic Univ. (United States)
Michael J. Medley, SUNY Polytechnic Institute (United States)


Published in SPIE Proceedings Vol. 10989:
Big Data: Learning, Analytics, and Applications
Fauzia Ahmad, Editor(s)

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