Coselection of features and instances for unsupervised rare category analysis

Jingrui He, Jaime Carbonell

Research output: Contribution to journalArticle

10 Scopus citations

Abstract

Rare category analysis is of key importance both in theory and in practice. Previous research work focuses on supervised rare category analysis, such as rare category detection and rare category classification. In this paper, for the first time, we address the challenge of unsupervised rare category analysis, including feature selection and rare category selection. We propose to jointly deal with the two correlated tasks, so that they can benefit from each other. To this end, we design an optimization framework which is able to coselect the relevant features and the examples from the rare category (a.k.a. the minority class). It is well justified theoretically. Furthermore, we develop the Partial Augmented Lagrangian Method (PALM) to solve the optimization problem. Experimental results on both synthetic and real data sets show the effectiveness of the proposed method.

Original languageEnglish (US)
Pages (from-to)417-430
Number of pages14
JournalStatistical Analysis and Data Mining
Volume3
Issue number6
DOIs
StatePublished - Dec 1 2010

Keywords

  • Augmented Lagrangian
  • Feature selection
  • Optimization
  • Rare category
  • Unsupervised

ASJC Scopus subject areas

  • Analysis
  • Information Systems
  • Computer Science Applications

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