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Non-negative matrix factorization with Gaussian process priors

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If you have a question about this talk, please contact Zoubin Ghahramani.

Non-negative matrix factorization (NMF) is a recent method for analyzing matrices of non-negative data. Many physical signals, such as pixel intensities, amplitude spectra, and occurence counts, are naturally represented by non-negative numbers, and in the analysis of mixtures of such data, non-negativity of the individual components is a reasonable constraint. NMF has found widespread use for pattern recognition, clustering, dimensionality reduction in many fields including audio signal processing, image processing, chemometrics and bioinformatics. The first part of this talk gives a general introduction to NMF , with examples from audio signal separation and DNA microarray analysis. The second part of the talk introduces Gaussian process priors in the NMF framework, illustrated with an example from chemical shift brain imaging.

This talk is part of the Machine Learning @ CUED series.

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