Dynamics of ICA for high-dimensional data

Gleb Basalyga, Magnus Rattray

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


    The learning dynamics close to the initial conditions of an on-line Hebbian ICA algorithm has been studied. For large input dimension the dynamics can be described by a diffusion equation.A surprisingly large number of examples and unusually low initial learning rate are required to avoid a stochastic trapping state near the initial conditions. Escape from this state results in symmetry breaking and the algorithm therefore avoids trapping in plateau-like fixed points which have been observed in other learning algorithms. © Springer-Verlag Berlin Heidelberg 2002.
    Original languageEnglish
    Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|Lect. Notes Comput. Sci.
    PublisherSpringer Nature
    Number of pages6
    ISBN (Print)9783540440741
    Publication statusPublished - 2002
    Event2002 International Conference on Artificial Neural Networks, ICANN 2002 - Madrid
    Duration: 1 Jul 2002 → …

    Publication series

    NameLecture Notes in Computer Science


    Conference2002 International Conference on Artificial Neural Networks, ICANN 2002
    Period1/07/02 → …
    Internet address


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