Regression with Sparse Approximations of Data

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Abstract

We propose sparse approximation weighted regression (SPARROW), a method for local estimation of the regression function that uses sparse approximation with a dictionary of measurements. SPARROW estimates the regression function at a point with a linear combination of a few regressands selected by a sparse approximation of the point in terms of the regressors. We show SPARROW can be considered a variant of \(k\)-nearest neighbors regression (\(k\)-NNR), and more generally, local polynomial kernel regression. Unlike \(k\)-NNR, however, SPARROW can adapt the number of regressors to use based on the sparse approximation process. Our experimental results show the locally constant form of SPARROW performs competitively.
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We propose sparse approximation weighted regression (SPARROW), a method for local estimation of the regression function that uses sparse approximation with a dictionary of measurements. SPARROW estimates the regression function at a point with a linear combination of a few regressands selected by a sparse approximation of the point in terms of the regressors. We show SPARROW can be considered a variant of \(k\)-nearest neighbors regression (\(k\)-NNR), and more generally, local polynomial kernel regression. Unlike \(k\)-NNR, however, SPARROW can adapt the number of regressors to use based on the sparse approximation process. Our experimental results show the locally constant form of SPARROW performs competitively.
Original languageEnglish
JournalProceedings of the European Signal Processing Conference
Volume2012
Pages (from-to)674-678
Number of pages5
ISSN2076-1465
StatePublished - 2012
Publication categoryResearch
Peer-reviewedYes
EventEUSIPCO2012 - Bucharest, Romania
Duration: 27 Aug 2012 → …

Conference

ConferenceEUSIPCO2012
CountryRomania
CityBucharest
Period27/08/2012 → …

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