dorsal/arxiv
View SchemaRegularization Strategies for Hyperplane Classifiers: Application to Cancer Classification with Gene Expression Data
| Authors | Erik Andries, Thomas Hagstrom, Susan R. Atlas, Cheryl Willman |
|---|---|
| Categories | |
| ArXiv ID | q-bio/0601002 |
| URL | https://arxiv.org/abs/q-bio/0601002 |
Abstract
Linear discrimination, from the point of view of numerical linear algebra, can be treated as solving an ill-posed system of linear equations. In order to generate a solution that is robust in the presence of noise, these problems require regularization. Here, we examine the ill-posedness involved in the linear discrimination of cancer gene expression data with respect to outcome and tumor subclasses. We show that a filter factor representation, based upon Singular Value Decomposition, yields insight into the numerical ill-posedness of the hyperplane-based separation when applied to gene expression data. We also show that this representation yields useful diagnostic tools for guiding the selection of classifier parameters, thus leading to improved performance.
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"abstract": "Linear discrimination, from the point of view of numerical linear algebra,\ncan be treated as solving an ill-posed system of linear equations. In order to\ngenerate a solution that is robust in the presence of noise, these problems\nrequire regularization. Here, we examine the ill-posedness involved in the\nlinear discrimination of cancer gene expression data with respect to outcome\nand tumor subclasses. We show that a filter factor representation, based upon\nSingular Value Decomposition, yields insight into the numerical ill-posedness\nof the hyperplane-based separation when applied to gene expression data. We\nalso show that this representation yields useful diagnostic tools for guiding\nthe selection of classifier parameters, thus leading to improved performance.",
"arxiv_id": "q-bio/0601002",
"authors": [
"Erik Andries",
"Thomas Hagstrom",
"Susan R. Atlas",
"Cheryl Willman"
],
"categories": [
"q-bio.GN"
],
"title": "Regularization Strategies for Hyperplane Classifiers: Application to Cancer Classification with Gene Expression Data",
"url": "https://arxiv.org/abs/q-bio/0601002"
},
"schema_id": "dorsal/arxiv",
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"variant": "snapshot-2026-03-01",
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