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Title:Semi-Supervised Regression using Spectral Techniques
Author(s):Cai, Deng; He, Xiaofei; Han, Jiawei
Subject(s):computer science
Abstract:Graph-based approaches for semi-supervised learning have received increasing amount of interest in recent years. Despite their good performance, many pure graph based algorithms do not have explicit functions and can not predict the label of unseen data. Graph regularization is a recently proposed framework which incorporates the intrinsic geometrical structure as a regularization term. It can be performed as semi-supervised learning when unlabeled samples are available. However, our theoretical analysis shows that such approach may not be optimal for multi-class problems. In this paper, we propose a novel method, called {\bf Spectral Regression} (SR). By using spectral techniques, we first compute a set of responses for each sample which respects both the label information and geometrical structure. Once the responses are obtained, the ordinary ridge regression can be apply to find the regression functions. Our proposed algorithm is particularly designed for multi-class problem. Experimental results on two real world classification problems arising in visual and speech recognition demonstrate the effectiveness of our algorithm.
Issue Date:2006-07
Genre:Technical Report
Other Identifier(s):UIUCDCS-R-2006-2749
Rights Information:You are granted permission for the non-commercial reproduction, distribution, display, and performance of this technical report in any format, BUT this permission is only for a period of 45 (forty-five) days from the most recent time that you verified that this technical report is still available from the University of Illinois at Urbana-Champaign Computer Science Department under terms that include this permission. All other rights are reserved by the author(s).
Date Available in IDEALS:2009-04-21

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