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ICA
2004
Springer

Non-linear ICA by Using Isometric Dimensionality Reduction

9 years 5 months ago
Non-linear ICA by Using Isometric Dimensionality Reduction
In usual ICA methods, sources are typically estimated by maximizing a measure of their statistical independence. This paper explains how to perform non-linear ICA by preprocessing the mixtures with recent non-linear dimensionality reduction techniques. These techniques are intended to produce a low-dimensional representation of the data (the mixtures), which is isometric to their initial high-dimensional distribution. A detailed study of the mixture model that makes the separation possible precedes a practical example.
John Aldo Lee, Christian Jutten, Michel Verleysen
Added 01 Jul 2010
Updated 01 Jul 2010
Type Conference
Year 2004
Where ICA
Authors John Aldo Lee, Christian Jutten, Michel Verleysen
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