| DOI | Resolve DOI: https://doi.org/10.1007/978-3-540-27868-9_77 |
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| Author | Search for: Pranckeviciene, Erinija1; Search for: Baumgartner, Richard1; Search for: Somorjai, Ray1; Search for: Bowman, Christopher1 |
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| Affiliation | - National Research Council of Canada. NRC Institute for Biodiagnostics
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| Format | Text, Book Chapter |
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| Conference | Joint IAPR International Workshops on Structural and Syntactical Pattern Recognition (SSPR 2004) and Statistical Pattern Recognition (SPR 2004), August 18-20, 2004, Lisbon, Portugal |
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| Abstract | In linear discriminant (LD) analysis high sample size/feature ratio is desirable. The linear programming procedure (LP) for LD identification handles the curse of dimensionality through simultaneous minimization of the L1 norm of the classification errors and the LD weights. The sparseness of the solution – the fraction of features retained – can be controlled by a parameter in the objective function. By qualitatively analyzing the objective function and the constraints of the problem, we show why sparseness arises. In a sparse solution, large values of the LD weight vector reveal those individual features most important for the decision boundary. |
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| Publication date | 2004 |
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| Publisher | Springer Berlin Heidelberg |
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| In | |
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| Series | |
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| Language | English |
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| Peer reviewed | Yes |
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| NRC number | NRC-IBD-2139 |
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| NPARC number | 9147559 |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | cbdae675-bf2f-48f7-8a3a-9f4c94013e2d |
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| Record created | 2009-06-25 |
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| Record modified | 2020-06-12 |
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