Boosting simple projections for multi-class dimensionality reduction

Yuan Yuan, Yanwei Pang

Research output: Contribution to journalConference articlepeer-review

2 Scopus citations

Abstract

This paper presents a novel method for dimensionality reduction and for multi-class classification tasks. This method iteratively selects a series of simple but effective 1D subspaces, and then combines the corresponding 1D projections by Adaboost. M2. Its major advantages are: 1) it does not impose speci.c assumptions on data distribution; 2) it minimizes Bayes error estimation in low-dimensional space; 3) it simpli.es existing subspace-based methods to eigenvalue decomposition problem; and 4) each of the 1D subspaces (with associated nearest neighbor classi.er) has different emphasis - measured by weighted training error. Experiments on both synthetic and real-world data demonstrate the effectiveness of the proposed method.

Original languageEnglish
Article number4811624
Pages (from-to)2231-2235
Number of pages5
JournalConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
DOIs
StatePublished - 2008
Externally publishedYes
Event2008 IEEE International Conference on Systems, Man and Cybernetics, SMC 2008 - Singapore, Singapore
Duration: 12 Oct 200815 Oct 2008

Keywords

  • Adaboost.M2
  • Feature extraction
  • Multi-class classification

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