Identification of kronecker-structured dictionaries: An asymptotic analysis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

The focus of this work is on derivation of conditions for asymptotic recovery of Kronecker-structured dictionaries underlying second-order tensor data. Given second-order tensor observations (equivalently, matrix-valued data samples) that are generated using a Kronecker-structured dictionary and sparse coefficient tensors, conditions on the dictionary and coefficient distribution are derived that enable asymptotic recovery of the individual coordinate dictionaries comprising the Kronecker dictionary within a local neighborhood of the true model. These conditions constitute the first step towards understanding the sample complexity of Kronecker-structured dictionary learning for second- and higher-order tensor data.

Original languageEnglish (US)
Title of host publication2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-5
Number of pages5
ISBN (Electronic)9781538612514
DOIs
StatePublished - Mar 9 2018
Event7th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017 - Curacao
Duration: Dec 10 2017Dec 13 2017

Publication series

Name2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017
Volume2017-December

Conference

Conference7th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017
CityCuracao
Period12/10/1712/13/17

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Control and Optimization
  • Instrumentation

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