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Activity Number:
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254
- Contributed Poster Presentations: Section on Statistical Learning and Data Science
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Type:
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Contributed
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Date/Time:
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Monday, July 30, 2018 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistical Learning and Data Science
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Abstract #329748
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Title:
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Sparse Variable Selection in Kernel Discriminant Analysis via Optimal Scoring
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Author(s):
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Alexander Lapanowski* and Irina Gaynanova
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Companies:
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Texas A&M and Texas A&M Univeristy
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Keywords:
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Reproducing kernel Hilbert Space;
Variable selection;
Kernel classification;
Kernel ridge regression;
LASSO
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Abstract:
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We consider the two-group classification problem and propose a new nonparametric algorithm for simultaneous variable selection and nonlinear dimension reduction. Our approach is based on applying the kernel trick within an optimal scoring framework and imposing structured sparsity using weighted kernels. The use of an optimal scoring framework enables efficient computations, and sparsity allows the removal of noisy features from the classification rule. Numerical studies demonstrate the superior classification performance of the proposed approach compared to common nonparametric classifiers. We support our empirical findings with theoretical guarantees on the expected risk consistency of the method.
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Authors who are presenting talks have a * after their name.
