Regression with Sparse Approximations of Data

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Regression with Sparse Approximations of Data. / Noorzad, Pardis; Sturm, Bob L.

In: Proceedings of the European Signal Processing Conference, Vol. 2012, 2012, p. 674-678 .

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@inproceedings{87d9a6cf824d4006b7be23fed96b6f58,
title = "Regression with Sparse Approximations of Data",
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.",
author = "Pardis Noorzad and Sturm, {Bob L.}",
year = "2012",
language = "English",
volume = "2012",
pages = "674--678",
journal = "Proceedings of the European Signal Processing Conference",
issn = "2076-1465",
publisher = "European Association for Signal Processing (EURASIP)",

}

RIS

TY - GEN

T1 - Regression with Sparse Approximations of Data

AU - Noorzad,Pardis

AU - Sturm,Bob L.

PY - 2012

Y1 - 2012

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84869790257&partnerID=8YFLogxK

M3 - Conference article in Journal

VL - 2012

SP - 674

EP - 678

JO - Proceedings of the European Signal Processing Conference

T2 - Proceedings of the European Signal Processing Conference

JF - Proceedings of the European Signal Processing Conference

SN - 2076-1465

ER -

ID: 71866590