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Non-parametric e-mixture of Density Functions Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Kernel L1-Minimization: Application to Kernel Sparse Representation Based Classification

Authors : Anupriya Gogna, Angshul Majumdar

Published in: Neural Information Processing

Publisher: Springer International Publishing

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Abstract

The sparse representation based classification (SRC) was initially proposed for face recognition problems. However, SRC was found to excel in a variety of classification tasks. There have been many extensions to SRC, of which group SRC, kernel SRC being the prominent ones. Prior methods in kernel SRC used greedy methods like Orthogonal Matching Pursuit (OMP). It is well known that for solving a sparse recovery problem, both in theory and in practice, l 1 -minimization is a better approach compared to OMP. The standard l 1 -minimization is a solved problem. For the first time in this work, we propose a technique for Kernel l 1 -minimization. Through simulation results we show that our proposed method outperforms prior kernelised greedy sparse recovery techniques.

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Metadata
Title
Kernel L1-Minimization: Application to Kernel Sparse Representation Based Classification
Authors
Anupriya Gogna
Angshul Majumdar
Copyright Year
2016
Book
Neural Information Processing

Print ISBN: 978-3-319-46671-2

Electronic ISBN: 978-3-319-46672-9

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-46672-9_16

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