JACIII Vol.11 No.9 pp. 1159-1164
doi: 10.20965/jaciii.2007.p1159


Semi-Supervised Pattern Classification Utilizing Fuzzy Clustering and Nonlinear Mapping of Data

Weiwei Du and Kiichi Urahama

Department of Visual Communication Design, Kyushu University, 4-9-1 Shiobaru, Fukuoka 815-8540, Japan

May 26, 2007
July 12, 2007
November 20, 2007
semi-supervised learning, fuzzy clustering, RBF mapping, locality preserving projection

We present a semi-supervised algorithm for classification of arbitrarily distributed patterns. We project data into a classification space through two stages, first is a nonlinear mapping with radial basis functions and second is a linear projection with a semi-supervised locality preserving projection. Radial basis functions are arranged by fuzzy clustering of training data. This fuzzy clustering is also exploited for selection of data to be labeled for semi-supervised learning. We devise a simple semi-supervised algorithm in which data similarity is multiplicatively modulated on the basis of label information. We examine performance of the proposed classifier with experiments for synthetic and some real data and show that our method outperforms similar graph spectral algorithms and kernel semi-supervised methods.

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Last updated on Jul. 29, 2016