Error-correcting semi-supervised learning with extended mode filter on graphs

Weiwei Du, Kiichi Urahama

Research output: Contribution to conferencePaperpeer-review

Abstract

We present a robust semi-supervised method using the extended mode filter for learning with partially-labeled training data including label errors. The mode filter was originally developed for smoothing images contaminated with impulsive noises and usually needs iterative solution methods. In this paper, we propose a direct solution method with full search of solution spaces. This direct method outperforms the iterative algorithm in classification rates and computational speeds. Additional iterations of the mode filter raise up the classification rates. We extend the mode filter by introducing weights based on the isolation degree of data, and show the effectiveness of this extension by UCI benchmark data and UMIST Face Database.

Original languageEnglish
Pages152-155
Number of pages4
Publication statusPublished - Dec 1 2010
EventJoint 5th International Conference on Soft Computing and Intelligent Systems and 11th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2010 - Okayama, Japan
Duration: Dec 8 2010Dec 12 2010

Conference

ConferenceJoint 5th International Conference on Soft Computing and Intelligent Systems and 11th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2010
CountryJapan
CityOkayama
Period12/8/1012/12/10

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Information Systems

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