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 - 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
    Country/TerritoryJapan
    CityOkayama
    Period12/8/1012/12/10

    All Science Journal Classification (ASJC) codes

    • Artificial Intelligence
    • Information Systems

    Fingerprint

    Dive into the research topics of 'Error-correcting semi-supervised learning with extended mode filter on graphs'. Together they form a unique fingerprint.

    Cite this