Damped Gauss-Newton algorithm for nonnegative Tucker decomposition

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14 Citations (Scopus)

Abstract

Algorithms based on alternating optimization for nonnegative Tucker decompositions (NTD) such as ALS, multiplicative least squares, HALS have been confirmed effective and efficient. However, those algorithms often converge very slowly. To this end, we propose a novel algorithm for NTD using the Levenberg-Marquardt technique with fast computation method to construct the approximate Hessian and gradient without building up the large-scale Jacobian. The proposed algorithm has been verified to overwhelmingly outperform state-of-the-art NTD algorithms for difficult benchmarks, and application of face clustering.

Original languageEnglish
Title of host publication2011 IEEE Statistical Signal Processing Workshop, SSP 2011
Pages665-668
Number of pages4
DOIs
Publication statusPublished - 2011
Externally publishedYes
Event2011 IEEE Statistical Signal Processing Workshop, SSP 2011 - Nice, France
Duration: 28 Jun 201130 Jun 2011

Publication series

NameIEEE Workshop on Statistical Signal Processing Proceedings

Conference

Conference2011 IEEE Statistical Signal Processing Workshop, SSP 2011
Country/TerritoryFrance
CityNice
Period28/06/1130/06/11

Keywords

  • face clustering
  • Gauss-Newton
  • Levenberg-Marquardt
  • low rank approximation
  • nonnegative Tucker decomposition

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