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- W2240135441 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." @default.
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- W2240135441 date "2004-01-01" @default.
- W2240135441 modified "2023-09-25" @default.
- W2240135441 title "Non-linear ICA by Using Isometric Dimensionality Reduction" @default.
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- W2240135441 doi "https://doi.org/10.1007/978-3-540-30110-3_90" @default.
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