Abstract
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.
Original language | English |
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Pages (from-to) | 259-266 |
Number of pages | 8 |
Journal | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
Volume | 5441 |
DOIs | |
State | Published - 2009 |
Externally published | Yes |
Event | 8th International Conference on Independent Component Analysis and Signal Separation, ICA 2009 - Paraty, Brazil Duration: 15 Mar 2009 → 18 Mar 2009 |