Conference Proceedings

ON DATA SPARSIFICATION AND A RECURSIVE ALGORITHM FOR ESTIMATING A KERNEL-BASED MEASURE OF INDEPENDENCE

Pierre-Olivier Amblard, Jonathan H Manton

Proceedings of the 38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP) | IEEE | Published : 2013

Abstract

Technological improvements have led to situations where data sets are sufficiently rich that in the interests of processing speed it is desirable to throw away samples that provide little additional information. This is referred to here as data sparsification. The first contribution is a study of a recently proposed data sparsification scheme; ideas from vector quantisation are used to assess its performance. Informed by this study, a modification of the data sparsification algorithm is proposed and applied to the problem of estimating a kernel-based measure of independence of two datasets. (Given i.i.d. observations from two random variables, x and y, the underlying problem is to determine ..

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Grants

Funding Acknowledgements

P. O. Amblard is funded by a Marie Curie International Outgoing Fellowship from the European Community.