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

Konstantinos Tountas; Dimitris A. Pados; Michael J. Medley

doi:10.1117/12.2520538

13 May 2019 Conformity evaluation and L₁-norm principal-component analysis of tensor data

Konstantinos Tountas, Dimitris A. Pados, Michael J. Medley

Proceedings Volume 10989, Big Data: Learning, Analytics, and Applications; 109890P (2019) https://doi.org/10.1117/12.2520538
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, MD, United States

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.

Citation Download Citation

Konstantinos Tountas, Dimitris A. Pados, and Michael J. Medley "Conformity evaluation and L₁-norm principal-component analysis of tensor data", Proc. SPIE 10989, Big Data: Learning, Analytics, and Applications, 109890P (13 May 2019); https://doi.org/10.1117/12.2520538

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