A fast importance sampling algorithm for unsupervised learning of over-complete dictionaries
Blumensath, T. and Davies, M.E. (2005) A fast importance sampling algorithm for unsupervised learning of over-complete dictionaries. In, IEEE International Conference on Acoustics, Speech and Signal Processing, Philadelphia, US, 18 - 23 Mar 2005. Institute of Electrical and Electronics Engineers , 213-216.
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Description/Abstract
We use Bayesian statistics to study the dictionary learning problem
in which an over-complete generative signal model has to be
adapted for optimally sparse signal representations. With such a
formulation we develop a stochastic gradient learning algorithm
based on Importance Sampling techniques to minimise the negative
marginal log-likelihood. As this likelihood is not available
analytically, approximations have to be utilised. The Importance
Sampling Monte Carlo marginalisation proposed here improves on
previous methods and addresses three main issues: 1) bias of the
gradient estimate; 2) multi-modality of the distribution to be approximated;
and 3) computational efficiency. Experimental results
show the advantages of the new method when compared to previous
techniques. The gained efficiency allows the treatment of large
scale problems in a statistically sound framework as demonstrated
here by the extraction of individual piano notes from a polyphonic
piano recording
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Related URLs: | |
| Subjects: | Q Science > QA Mathematics Q Science > QC Physics |
| Divisions: | University Structure - Pre August 2011 > School of Mathematics Faculty of Engineering and the Environment > Institute of Sound and Vibration Research > Signal Processing & Control Research Group |
| Item ID: | 151935 |
| Date Deposited: | 01 Jul 2010 10:42 |
| Last Modified: | 11 Sep 2012 13:53 |
| Contributors: | Blumensath, T. (Author) Davies, M.E. (Author) |
| Date: | March 2005 |
| Status: | Published |
| Publisher: | Institute of Electrical and Electronics Engineers |
| URI: | http://eprints.soton.ac.uk/id/eprint/151935 |
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