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Proceedings Paper

Options for multimodal classification based on L1-Tucker decomposition
Author(s): Dimitris G. Chachlakis; Mayur Dhanaraj; Ashley Prater-Bennette; Panos P. Markopoulos
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Paper Abstract

Most commonly used classification algorithms process data in the form of vectors. At the same time, mod- ern datasets often comprise multimodal measurements that are naturally modeled as multi-way arrays, also known as tensors. Processing multi-way data in their tensor form can enable enhanced inference and classification accuracy. Tucker decomposition is a standard method for tensor data processing, which however has demonstrated severe sensitivity to corrupted measurements due to its L2-norm formulation. In this work, we present a selection of classification methods that employ an L1-norm-based, corruption-resistant reformulation of Tucker (L1-Tucker). Our experimental studies on multiple real datasets corroborate the corruption-resistance and classification accuracy afforded by L1-Tucker.

Paper Details

Date Published: 13 May 2019
PDF: 13 pages
Proc. SPIE 10989, Big Data: Learning, Analytics, and Applications, 109890O (13 May 2019); doi: 10.1117/12.2520140
Show Author Affiliations
Dimitris G. Chachlakis, Rochester Institute of Technology (United States)
Mayur Dhanaraj, Rochester Institute of Technology (United States)
Ashley Prater-Bennette, Air Force Research Lab. (United States)
Panos P. Markopoulos, Rochester Institute of Technology (United States)


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

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