Počet záznamů: 1

Fast damped Gauss-Newton algorithm for sparse and nonnegative tensor factorization

  1. 1.
    0360026 - UTIA-B 2012 RIV US eng C - Konferenční příspěvek (zahraniční konf.)
    Phan, A. H. - Tichavský, Petr - Cichocki, A.
    Fast damped Gauss-Newton algorithm for sparse and nonnegative tensor factorization.
    Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing 2011. Piscataway: IEEE, 2011, s. 1988-1991. ISBN 978-1-4577-0539-7.
    [2011 IEEE International Conference on Acoustics, Speech, and Signal Processing ICASSP 2011. Praha (CZ), 22.05.2011-27.05.2011]
    Grant CEP: GA MŠk 1M0572; GA ČR GA102/09/1278
    Výzkumný záměr: CEZ:AV0Z10750506
    Klíčová slova: Multilinear models * canonical polyadic decomposition * nonegative tensor factorization
    Kód oboru RIV: BB - Aplikovaná statistika, operační výzkum
    http://library.utia.cas.cz/separaty/2011/SI/tichavsky-fast damped gauss-newton  algorithm for nonnegative matrix factorization.pdf http://library.utia.cas.cz/separaty/2011/SI/tichavsky-fast damped gauss-newton algorithm for nonnegative matrix factorization.pdf

    Alternating optimization algorithms for canonical polyadic decomposition (with/without nonnegative constraints) often accompany update rules with low computational cost, but could face problems of swamps, bottlenecks, and slow convergence. All-at-once algorithms can deal with such problems, but always demand significant temporary extra-storage, and high computational cost. In this paper, we propose an allat- once algorithmwith lowcomplexity for sparse and nonnegative tensor factorization based on the damped Gauss-Newton iteration. Especially, for low-rank approximations, the proposed algorithm avoids building up Hessians and gradients, reduces the computational cost dramatically. Moreover, we proposed selection strategies for regularization parameters. The proposed algorithm has been verified to overwhelmingly outperform “state-of-the-art” NTF algorithms for difficult benchmarks, and for real-world application such as clustering of the ORL face database.
    Trvalý link: http://hdl.handle.net/11104/0197677