Learning filters in gaussian process classification problems

Pablo Ruiz, Javier Mateos, Rafael Molina, Aggelos K. Katsaggelos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Many real classification tasks are oriented to sequence (neighbor) labeling, that is, assigning a label to every sample of a signal while taking into account the sequentiality (or neighborhood) of the samples. This is normally approached by first filtering the data and then performing classification. In consequence, both processes are optimized separately, with no guarantee of global optimality. In this work we utilize Bayesian modeling and inference to jointly learn a classifier and estimate an optimal filterbank. Variational Bayesian inference is used to approximate the posterior distributions of all unknowns, resulting in an iterative procedure to estimate the classifier parameters and the filterbank coefficients. In the experimental section we show, using synthetic and real data, that the proposed method compares favorably with other classification/filtering approaches, without the need of parameter tuning.
Original languageEnglish
Title of host publicationProceedings of IEEE International Conference on Image Processing
StatePublished - 2014
EventProceedings of IEEE International Conference on Image Processing - Paris, France
Duration: Oct 27 2014 → …

Conference

ConferenceProceedings of IEEE International Conference on Image Processing
Period10/27/14 → …

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