Szabo, Z;
Lorincz, A;
(2006)
Real and Complex Independent Subspace Analysis by Generalized Variance.
In:
ICA Research Network International Workshop (ICARN).
(pp. 85 - 88).
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Abstract
Here, we address the problem of Independent Subspace Analysis (ISA). We develop a technique that (i) builds upon joint decorrelation for a set of functions, (ii) can be related to kernel based techniques, (iii) can be interpreted as a self-adjusting, self-grouping neural network solution, (iv) can be used both for real and for complex problems, and (v) can be a first step towards large scale problems. Our numerical examples extend to a few 100 dimensional ISA tasks.
| Type: | Proceedings paper |
|---|---|
| Title: | Real and Complex Independent Subspace Analysis by Generalized Variance |
| Event: | 2006 ICA Research Network International Workshop (ICARN) |
| Open access status: | An open access version is available from UCL Discovery |
| Language: | English |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences |
| URI: | https://discovery.ucl.ac.uk/id/eprint/1433234 |
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