TY - GEN
T1 - Error-correcting semi-supervised learning with mode-filter on graphs
AU - Du, Weiwei
AU - Urahama, Kiichi
PY - 2009/12/1
Y1 - 2009/12/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=77953227280&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77953227280&partnerID=8YFLogxK
U2 - 10.1109/ICCVW.2009.5457539
DO - 10.1109/ICCVW.2009.5457539
M3 - Conference contribution
AN - SCOPUS:77953227280
SN - 9781424444427
T3 - 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009
SP - 2095
EP - 2100
BT - 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009
T2 - 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009
Y2 - 27 September 2009 through 4 October 2009
ER -