Options for multimodal classification based on L1-Tucker decomposition

Dimitris G. Chachlakis; Mayur Dhanaraj; Ashley Prater-Bennette; Panos P. Markopoulos

doi:10.1117/12.2520140

13 May 2019 Options for multimodal classification based on L1-Tucker decomposition

Dimitris G. Chachlakis, Mayur Dhanaraj, Ashley Prater-Bennette, Panos P. Markopoulos

Proceedings Volume 10989, Big Data: Learning, Analytics, and Applications; 109890O (2019) https://doi.org/10.1117/12.2520140
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, MD, United States

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.

Citation Download Citation

Dimitris G. Chachlakis, Mayur Dhanaraj, Ashley Prater-Bennette, and Panos P. Markopoulos "Options for multimodal classification based on L1-Tucker decomposition", Proc. SPIE 10989, Big Data: Learning, Analytics, and Applications, 109890O (13 May 2019); https://doi.org/10.1117/12.2520140

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