SSPS: A Semi-Supervised Pattern Shift for Classification

Authors:
Enliang Hu;Xuesong Yin;Yongming Wang;Songcan Chen
Affiliations:
School of Information, Yunnan University of Finance and Economics, Kunming, China 650221 and School of Computer Science & Engineering, Nanjing University of Aeronautics & Astronautics, Nanjing, Ch ...;School of Computer Science & Engineering, Nanjing University of Aeronautics & Astronautics, Nanjing, China 210016;School of Management and Economics, Kunming University of Science & Technology, Kunming, China 650093;School of Computer Science & Engineering, Nanjing University of Aeronautics & Astronautics, Nanjing, China 210016
Venue:
Neural Processing Letters
Year:
2010

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Abstract

Recently, a great amount of efforts have been spent in the research of unsupervised and (semi-)supervised dimensionality reduction (DR) techniques, and DR as a preprocessor is widely applied into classification learning in practice. However, on the one hand, many DR approaches cannot necessarily lead to a better classification performance. On the other hand, DR often suffers from the problem of estimation of retained dimensionality for real-world data. Alternatively, in this paper, we propose a new semi-supervised data preprocessing technique, named semi-supervised pattern shift (SSPS). The advantages of SSPS lie in the fact that not only the estimation of retained dimensionality can be avoided naturally, but a new shifted pattern representation that may be more favorable to classification is obtained as well. As a further extension of SSPS, we develop its fast and out-of-sample versions respectively, both of which are based on a shape-preserved subset selection trick. The final experimental results demonstrate that the proposed SSPS is promising and effective in classification application.