Feature subspace SVMs (FS-SVMs) for high dimensional handwritten digit recognition

Vikas K. Garg*, M. N. Murty

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

10 Citations (Scopus)

Abstract

Training SVMs on high dimensional feature vectors in one shot incurs high computational cost. A low dimensional representation reduces computational overhead and improves the classification speed. Low dimensionality also reduces the risk of over-fitting and tends to improve the generalisation ability of classification algorithms. For many important applications, the dimensionality may remain prohibitively high despite feature selection. In this paper, we address these issues primarily in the context of handwritten digit data. In particular, we make the following contributions: 1 we introduce the α-minimum feature over (α-MFC) problem and prove it to be NP-hard 2 investigate the efficacy of a divide-and-conquer ensemble method for SVMs based on segmentation of the feature space (FS-SVMs) 3 propose a greedy algorithm for finding an approximate α-MFC using FS-SVMs.

Original languageEnglish
Pages (from-to)411-436
Number of pages26
JournalINTERNATIONAL JOURNAL OF DATA MINING, MODELLING AND MANAGEMENT
Volume1
Issue number4
DOIs
Publication statusPublished - 2009
MoE publication typeA1 Journal article-refereed

Keywords

  • Approximation algorithms
  • Classification
  • Dimensionality reduction
  • Feature selection
  • Greedy algorithms
  • Support vector machines
  • SVMs

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