Abstract
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.
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© 2004 Springer-Verlag Berlin Heidelberg
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Lee, J.A., Jutten, C., Verleysen, M. (2004). Non-linear ICA by Using Isometric Dimensionality Reduction. In: Puntonet, C.G., Prieto, A. (eds) Independent Component Analysis and Blind Signal Separation. ICA 2004. Lecture Notes in Computer Science, vol 3195. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30110-3_90
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DOI: https://doi.org/10.1007/978-3-540-30110-3_90
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Print ISBN: 978-3-540-23056-4
Online ISBN: 978-3-540-30110-3
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