Semi-Supervised SVM with Extended Hidden Features

Aimei Dong, Fu Lai Korris Chung, Zhaohong Deng, Shitong Wang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

18 Citations (Scopus)

Abstract

Many traditional semi-supervised learning algorithms not only train on the labeled samples but also incorporate the unlabeled samples in the training sets through an automated labeling process such as manifold preserving. If some labeled samples are falsely labeled, the automated labeling process will generally propagate negative impact on the classifier in quite a serious manner. In order to avoid such an error propagating effect, the unlabeled samples should not be directly incorporated into the training sets during the automated labeling strategy. In this paper, a new semi-supervised support vector machine with extended hidden features (SSVM-EHF) is presented to address this issue. According to the maximum margin principle and the minimum integrated squared error between the probability distributions of the labeled and unlabeled samples, the dimensionality of the labeled and unlabeled samples is extended through an orthonormal transformation to generate the corresponding hidden features shared by the labeled and unlabeled samples. After doing so, the last step in the process of training of SSVM-EHF is done only on the labeled samples with their original and hidden features, and the unlabeled samples are no longer explicitly used. Experimental results confirm the effectiveness of the proposed method.
Original languageEnglish
Pages (from-to)2924-2937
Number of pages14
JournalIEEE Transactions on Cybernetics
Volume46
Issue number12
DOIs
Publication statusPublished - 1 Dec 2016

Keywords

  • Hidden features
  • integrated squared error between probability distributions
  • maximum margin
  • semi-supervised learning (SSL)
  • support vector machine (SVM)

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering

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