Kernel Feature Selection Export

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Abstract

We address the problem of using a kernel spectral criterion function for feature selection. A feature selection paradigm using spectral properties of the affinity matrix of the input data was recently introduced in [11] and which leads to a bilinear interaction between entries of the data sample. Our goal in this paper is to extend the idea of spectral criteria for feature selection to higher order interactions among the data by introducing a kernel operation through which high-order mappings of the original feature vectors can be made possible. 1

@misc{citeulike:3484798, abstract = {We address the problem of using a kernel spectral criterion function for feature selection. A feature selection paradigm using spectral properties of the affinity matrix of the input data was recently introduced in [11] and which leads to a bilinear interaction between entries of the data sample. Our goal in this paper is to extend the idea of spectral criteria for feature selection to higher order interactions among the data by introducing a kernel operation through which high-order mappings of the original feature vectors can be made possible. 1}, author = {Wolf, Lior and Shashua, Amnon}, citeulike-article-id = {3484798}, citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.3.8819}, keywords = {featurer, kernel, q-alpha, selection}, posted-at = {2008-11-06 14:44:34}, priority = {2}, title = {Kernel Feature Selection}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.3.8819} }

RIS record

TY - ELEC ID - citeulike:3484798 L3 - citeulike-article-id:3484798 N2 - We address the problem of using a kernel spectral criterion function for feature selection. A feature selection paradigm using spectral properties of the affinity matrix of the input data was recently introduced in [11] and which leads to a bilinear interaction between entries of the data sample. Our goal in this paper is to extend the idea of spectral criteria for feature selection to higher order interactions among the data by introducing a kernel operation through which high-order mappings of the original feature vectors can be made possible. 1 TI - Kernel Feature Selection KW - featurer KW - kernel KW - q-alpha KW - selection AU - Wolf, Lior AU - Shashua, Amnon UR - http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.3.8819 ER -

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text	10
classification	9
feature	9
mining	8
selection	7
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regularization	3
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Kernel Feature Selection Export

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