Damped Gauss-Newton algorithm for nonnegative Tucker Decomposition

0363810 - ÚTIA 2012 RIV FR eng C - Conference Paper (international conference)
Phan, A. H. - Tichavský, Petr - Cichocki, A.
Damped Gauss-Newton algorithm for nonnegative Tucker Decomposition.
2011 IEEE Statistical Signal Processing Workshop (SSP) Proceedings. Nice: IEEE Signal Processing Society, 2011, s. 669-672. ISBN 978-1-4577-0568-7.
[2011 IEEE Statistical Signal Processing Workshop (SSP). Nice (FR), 28.06.2011-30.06.2011]
R&D Projects: GA MŠMT 1M0572; GA ČR GA102/09/1278
Institutional research plan: CEZ:AV0Z10750506
Keywords : nonnegative Tucker decomposition * low-rank approximation * face clustering
Subject RIV: BB - Applied Statistics, Operational Research
http://library.utia.cas.cz/separaty/2011/SI/tichavsky-damped gauss-newton algorithm for nonnegative tucker decomposition.pdf

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.
Permanent Link: http://hdl.handle.net/11104/0199466