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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 At IEEE International Conference on Acoustics, Speech and Signal Processing, United States. 18 - 23 Mar 2005. , pp. 213-216.

Record type: Conference or Workshop Item (Paper)

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

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More information

Published date: March 2005
Venue - Dates: IEEE International Conference on Acoustics, Speech and Signal Processing, United States, 2005-03-18 - 2005-03-23
Organisations: Signal Processing & Control Grp

Identifiers

Local EPrints ID: 151935
URI: http://eprints.soton.ac.uk/id/eprint/151935
PURE UUID: 4da229bb-cb4c-4067-a54d-9b78842fd7e5

Catalogue record

Date deposited: 01 Jul 2010 10:42
Last modified: 18 Jul 2017 12:55

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