Spectral Energy Minimization for Semi-supervised Learning

Chun-hung Li, Zhi-li Wu

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

1 Citation (Scopus)


Data mining problems often involve a large amount of unlabeled data and there is often very limited known information on the dataset. In such scenario, semi-supervised learning can often improve classification performance by utilizing unlabeled data for learning. In this paper, we proposed a novel approach to semi-supervised learning as as an optimization of both the classification energy and cluster compactness energy in the unlabeled dataset. The resulting integer programming problem is relaxed by a semi-definite relaxation where efficient solution can be obtained. Furthermore, the spectral graph methods provide improved energy minimization via the incorporation of additional criteria. Results on UCI datasets show promising results.
Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining
Subtitle of host publication8th Pacific-Asia Conference, PAKDD 2004, Sydney, Australia, May 26-28, 2004, Proceedings
EditorsHonghua Dai, Ramakrishnan Srikant, Chengqi Zhang
PublisherSpringer Berlin Heidelberg
Number of pages9
ISBN (Electronic)9783540247753
ISBN (Print)9783540220640
Publication statusPublished - 22 Apr 2004
Event8th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2004 - Sydney, Australia
Duration: 26 May 200428 May 2004
https://link.springer.com/book/10.1007/b97861 (Conference proceedings)

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349
NameLecture Notes in Artificial Intelligence
NamePAKDD: Pacific-Asia Conference on Knowledge Discovery and Data Mining


Conference8th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2004
Abbreviated titlePAKDD 2004
Internet address

User-Defined Keywords

  • Spectral Method
  • Label Data
  • Unlabeled Data
  • Data Mining Problem
  • Transductive Support Vector Machine


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