Robust semi-supervised SVM on kernel partial least discriminant space for high dimensional data mining

研究成果: Conference contribution

摘要

Kernel machines (such as support vector machines) have demonstrated excellent performance in numerous areas of pattern recognitions. However, traditional kernel machines do not make efficient use of both labeled training data and unlabeled testing data. Moreover, high dimensional and nonlinear distributed data generally degrade the performance of kernel classifiers due to the curse of dimensionality. To address these problems, this study proposes a novel hybrid classifier which constructs a robust semi- supervised support vector machine (SVM) on kernel partial least square discriminant space (KPLSDS). KPLSDS is created by optimal projection of original data space to a representative low dimensional subspace which has maximum covariance between inputs and outputs. Robust semi-supervised SVMs on KPLSDS exploit the candidate low-density separators and simultaneously prevent identifying a poor separator from the help of unlabeled data. Compared with other dimensionality reduction methods and conventional classifiers, the hybrid classifier performs best.

原文English
主出版物標題2012 International Conference on Information Science and Applications, ICISA 2012
DOIs
出版狀態Published - 2012 七月 30
事件2012 International Conference on Information Science and Applications, ICISA 2012 - Suwon, Korea, Republic of
持續時間: 2012 五月 232012 五月 25

出版系列

名字2012 International Conference on Information Science and Applications, ICISA 2012

Other

Other2012 International Conference on Information Science and Applications, ICISA 2012
國家Korea, Republic of
城市Suwon
期間12-05-2312-05-25

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Information Systems

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