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

Weiwei Du, Kiichi Urahama

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    4 Citations (Scopus)

    Abstract

    We present a semi-supervised learning algorithm robust to label errors in training data. Our method employs the mode filter used for smoothing noisy images. We extend it from images to functions on graphs for regression of classification functions on an undirected graph. Our contribution in this paper lies in the introduction of nonlinearity in the regression in contrast to linear interpolation used in previous graph-based semi-supervised learning algorithms. Error-correcting effect of mode filters is demonstrated and the classification rates of the present learning method is evaluated with experiments for the UCI benchmark datasets contaminated with label errors.

    Original languageEnglish
    Title of host publication2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009
    Pages2095-2100
    Number of pages6
    DOIs
    Publication statusPublished - 2009
    Event2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009 - Kyoto, Japan
    Duration: Sept 27 2009Oct 4 2009

    Publication series

    Name2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009

    Other

    Other2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009
    Country/TerritoryJapan
    CityKyoto
    Period9/27/0910/4/09

    All Science Journal Classification (ASJC) codes

    • Computer Vision and Pattern Recognition
    • Electrical and Electronic Engineering

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