Hierarchy, priors and wavelets: structure and signal modelling using ICA
In many data analysis problems, it is useful to consider the data as generated from a set of unknown (latent) generators or sources. The observations we make of a system are then taken to be related to these sources through some unknown function. Furthermore, the (unknown) number of underlying laten...
| मुख्य लेखकों: | , , |
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| स्वरूप: | Journal article |
| भाषा: | English |
| प्रकाशित: |
2004
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| सारांश: | In many data analysis problems, it is useful to consider the data as generated from a set of unknown (latent) generators or sources. The observations we make of a system are then taken to be related to these sources through some unknown function. Furthermore, the (unknown) number of underlying latent sources may be less than the number of observations. Recent developments in independent component analysis (ICA) have shown that such data decomposition may be achieved in a mathematically elegant manner. In this paper, we extend the general ICA paradigm to include a very flexible source model, prior constraints and conditioning on sets of intermediate variables so that ICA forms one part of a hierarchical system. We show that such an approach allows for efficient unsupervised data partitioning and for sparse coding of signals using a hybrid wavelet-ICA model. © 2003 Elsevier B.V. All rights reserved. |
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