Gruber, Peter and Gutch, Harold W. and Theis, Fabian J. (2009) Hierarchical Extraction of Independent Subspaces of Unknown Dimensions. In: Adali, T., (ed.) Independent component analysis and signal separation, ICA 2009, Paraty, Brazil, March 15 - 18, 2009; proceedings. Lecture notes in computer science, 5441. Springer, Berlin, pp. 259-266. ISBN 978-3-642-00598-5 (print), 978-3-642-00599-2 (e-book).
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Independent Subspace Analysis (ISA) is an extension of Independent Component Analysis (ICA) that aims to linearly transform a random vector such as to render groups of its components mutually independent. A recently proposed fixed-point algorithm is able to locally perform ISA if the sizes of the subspaces are known, however global convergence is a serious problem as the proposed cost function has additional local minima. We introduce an extension to this algorithm, based on the idea that the algorithm converges to a solution, in which subspaces that are members of the global minimum occur with a higher frequency. We show that this overcomes the algorithm’s limitations. Moreover, this idea allows a blind approach, where no a priori knowledge of subspace sizes is required.
|Item Type:||Book Section|
|Institutions:||Biology, Preclinical Medicine > Institut für Biophysik und physikalische Biochemie > Prof. Dr. Elmar Lang|
|Subjects:||500 Science > 570 Life sciences|
|Created at the University of Regensburg:||Unknown|
|Deposited On:||01 Oct 2010 08:11|
|Last Modified:||01 Oct 2010 08:11|