Advances in Independent Component Analysis by William D. Penny, Richard M. Everson, Stephen J. Roberts

By William D. Penny, Richard M. Everson, Stephen J. Roberts (auth.), Mark Girolami BSc (Hons), BA, MSc, PhD, CEng, MIEE, MIMechE (eds.)

Independent part research (ICA) is a quick constructing region of excessive study curiosity. Following on from Self-Organising Neural Networks: self reliant part research and Blind sign Separation, this publication studies the numerous advancements of the prior year.
It covers subject matters resembling using hidden Markov tools, the independence assumption, and topographic ICA, and contains instructional chapters on Bayesian and variational ways. It additionally presents the newest methods to ICA difficulties, together with an research into definite "hard difficulties" for the first actual time.
Comprising contributions from the main revered and cutting edge researchers within the box, this quantity could be of curiosity to scholars and researchers in computing device technology and electric engineering; examine and improvement team of workers in disciplines comparable to statistical modelling and information research; bio-informatic staff; and physicists and chemists requiring novel facts research methods.

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Learning rates may be considerably enhanced by modifying the learning rule to make it covariant [1,17]. A common choice is p(sr) ex: 1/ cosh(sr), which leads to a tanh nonlinearity in the learning rule. Although the source model is apparently fixed, scaling of the mixing matrix tunes the model to particular sources [6] and, with a tanh nonlinearity, platykurtic (heavy-tailed) sources can be separated, although not leptokurtic ones. Cardoso [5] has elucidated the conditions under which the true mixing matrix is a stable fixed point of the learning rule.

Were equal for all q, Le. rinen et al of the likelihood can be expressed as T logL(wi,i Q = 1, ... 3) iES. where G(LiES sn = logpq(si' i E Sq) gives the logarithm of the probability density inside the q-th m-tuple of Si. The independent subspace model introduces a certain dependence structure for the independent components. Let us assume that the distribution in the subspace is sparse, which means that the norm of the projection is most of the time very near to zero; in other words, the norm has a distribution similar to the distribution of the absolute value of a super-Gaussian variable [4].

Dependencies that cannot be cancelled by ICA, could be used to define a topographic order between the components. The topographic order is easy to represent by visualization, and has the usual computational advantages associated with topographic maps [11,23,33]. We propose a modification of the ICA model that explicitly formalizes a topographic order between the independent components. This gives a topographic map where the distance of the components in the topographic representation is a function of the dependencies of the components.

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