Počet záznamů: 1

Simultanous search for all modes in multilinear models

  1. 1.
    0341572 - UTIA-B 2011 RIV US eng C - Konferenční příspěvek (zahraniční konf.)
    Tichavský, Petr - Koldovský, Zbyněk
    Simultanous search for all modes in multilinear models.
    Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing 2010. Dallas: IEEE, 2010, s. 4114-4117. ISBN 978-1-4244-4296-6.
    [2010 IEEE International Conference on Acoustics, Speech, and Signal Processing. Dallas, TX (US), 14.03.2010-19.03.2010]
    Grant CEP: GA MŠk 1M0572; GA ČR GA102/09/1278
    Grant ostatní: GA ČR(CZ) GP102/07/P384
    Výzkumný záměr: CEZ:AV0Z10750506
    Klíčová slova: tensor factorization * multilinear models
    Kód oboru RIV: BB - Aplikovaná statistika, operační výzkum
    http://library.utia.cas.cz/separaty/2010/SI/tichavsky-simultanous search for all modes in multilinear models.pdf http://library.utia.cas.cz/separaty/2010/SI/tichavsky-simultanous search for all modes in multilinear models.pdf

    Parallel factor (PARAFAC) analysis is an extension of a low rank decomposition to higher way arrays, usually called tensors. Most of existing methods are based on an alternating least square (ALS) algorithm that proceeds iteratively, and minimizes a criterion (that is usually quadratic) of the fit with respect to individual factors one by one. Convergence of this approach is known to be slow, if some of the factor contain nearly co-linear vectors. This problem can be partly alleviated by an enhanced line search (ELS) by Rajih et al. (2008). In this paper we show that the method originally proposed by Paatero (1997), consisting in optimization with respect to all modes simultaneously, can be simplified, and can far outperform the ALS-ELS in ill--conditioned data in all modes.
    Trvalý link: http://hdl.handle.net/11104/0184511