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Dynamic L1-norm Tucker Tensor Decomposition
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  • Dimitris G. Chachlakis ,
  • Mayur Dhanaraj ,
  • Ashley Prater-Bennette ,
  • Panos P. Markopoulos
Dimitris G. Chachlakis
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Mayur Dhanaraj
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Ashley Prater-Bennette
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Panos P. Markopoulos
Rochester Institute of Technology

Corresponding Author:[email protected]

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Abstract

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.
Apr 2021Published in IEEE Journal of Selected Topics in Signal Processing volume 15 issue 3 on pages 587-602. 10.1109/JSTSP.2021.3058846