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A Clustering-based Semi-Supervised Learning through Initial Prediction of Unlabeled Data  

Kim, Eung-Ku (한국생산성본부 컨설팅본부 CS경영센터)
Jun, Chi-Hyuck (포항공과대학교 산업경영공학과)
Publication Information
Journal of the Korean Operations Research and Management Science Society / v.33, no.3, 2008 , pp. 93-105 More about this Journal
Abstract
Semi-supervised learning uses a small amount of labeled data to predict labels of unlabeled data as well as to improve clustering performance, whereas unsupervised learning analyzes only unlabeled data for clustering purpose. We propose a new clustering-based semi-supervised learning method by reflecting the initial predicted labels of unlabeled data on the objective function. The initial prediction should be done in terms of a discrete probability distribution through a classification method using labeled data. As a result, clusters are formed and labels of unlabeled data are predicted according to the Information of labeled data in the same cluster. We evaluate and compare the performance of the proposed method in terms of classification errors through numerical experiments with blinded labeled data.
Keywords
Clustering; Labeled Data; Semi-supervised Learning; Unlabeled Data;
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