In *7th International Conference on Independent Component Analysis and Signal Separation (ICA)*, 2007.

Paper Pdf doi abstract bibtex

Paper Pdf doi abstract bibtex

A nonparametric Bayesian extension of Independent Components Analysis (ICA) is proposed where observed data Y is modelled as a linear superposition, G, of a potentially infinite number of hidden sources, X. Whether a given source is active for a specific data point is specified by an infinite binary matrix, Z. The resulting sparse representation allows increased data reduction compared to standard ICA. We define a prior on Z using the Indian Buffet Process (IBP). We describe four variants of the model, with Gaussian or Laplacian priors on X and the one or two-parameter IBPs. We demonstrate Bayesian inference under these models using a Markov Chain Monte Carlo (MCMC) algorithm on synthetic and gene expression data and compare to standard ICA algorithms.

@inproceedings{Knowles07iica, Abstract = {A nonparametric Bayesian extension of Independent Components Analysis (ICA) is proposed where observed data Y is modelled as a linear superposition, G, of a potentially infinite number of hidden sources, X. Whether a given source is active for a specific data point is specified by an infinite binary matrix, Z. The resulting sparse representation allows increased data reduction compared to standard ICA. We define a prior on Z using the Indian Buffet Process (IBP). We describe four variants of the model, with Gaussian or Laplacian priors on X and the one or two-parameter IBPs. We demonstrate Bayesian inference under these models using a Markov Chain Monte Carlo (MCMC) algorithm on synthetic and gene expression data and compare to standard ICA algorithms.}, Author = {Knowles, David A. and Ghahramani, Zoubin}, Booktitle = {7th International Conference on Independent Component Analysis and Signal Separation (ICA)}, Doi = {10.1007/978-3-540-74494-8}, Isbn = {978-3-540-74493-1}, Keywords = {Machine Learning/Statistics}, Title = {{Infinite Sparse Factor Analysis and Infinite Independent Components Analysis}}, Url = {http://www.springerlink.com/index/10.1007/978-3-540-74494-8}, Url_Pdf = {http://mlg.eng.cam.ac.uk/pub/pdf/KnoGha07.pdf}, Year = {2007}}

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