Technical Program

Paper Detail

Session:Learning Theory and Models
Time:Thursday, May 20, 15:30 - 17:30
Presentation: Poster
Topic: Machine Learning for Signal Processing: Signal detection, Pattern Recognition and Classification
Authors: Jeff Fortuna; McMaster University 
 David Capson; McMaster University 
Abstract: An algorithm that discovers a compact data representation for support vector classification is presented. The algorithm finds a basis which reduces the volume occupied by the coefficients in subspace. This volume reduction is driven by the support vectors of a support vector machine. A compact support vector representation (CSVR) of this form is shown to exhibit good generalization in the form of large margin and a small number of support vectors, while achieving low classification error rates. The compact nature of the data representation is shown to be particularly effective in representing correlated image sets such as those found in databases where faces and objects are imaged under varying lighting or pose.

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