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Dynamic L1-norm Tucker Tensor Decomposition

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posted on 07.08.2020, 17:09 by Dimitris G. Chachlakis, Mayur Dhanaraj, Ashley Prater-Bennette, Panos P. Markopoulos

Tucker decomposition is a standard method for processing multi-way (tensor) measurements and finds many applications in machine learning and data mining, among other fields. When tensor measurements arrive in a streaming fashion or are too many to jointly decompose, incremental Tucker analysis is preferred. In addition, dynamic basis adaptation is desired when the nominal data subspaces change. At the same time, it has been documented that outliers in the data can significantly compromise the performance of existing methods for dynamic Tucker analysis. In this work, we present Dynamic L1-Tucker: an algorithm for dynamic and outlier-resistant Tucker analysis of tensor data. Our experimental studies on both real and synthetic datasets corroborate that the proposed method (i) attains high basis estimation performance, (ii) identifies/rejects outliers, and (iii) adapts to nominal subspace changes.

Funding

National Science Foundation - OAC 1808582

Air Force Office of Scientific Research - 18RICOR029

Air Force Office of Scientific Research - YIP

History

Email Address of Submitting Author

pxmeee@rit.edu

ORCID of Submitting Author

0000-0001-9686-779X

Submitting Author's Institution

Rochester Institute of Technology

Submitting Author's Country

United States of America

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