Localized projection learning

Authors:
Kazuki Tsuji;Mineichi Kudo;Akira Tanaka
Affiliations:
Graduate School of Information Science and Technology, Hokkaido University, Sapporo, Japan;Graduate School of Information Science and Technology, Hokkaido University, Sapporo, Japan;Graduate School of Information Science and Technology, Hokkaido University, Sapporo, Japan
Venue:
SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
Year:
2010

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Abstract

It is interesting to compare different criteria of kernel machines. In this paper, the following is made: 1) to cope with the scaling problem of projection learning, we propose a dynamic localized projection learning using k nearest neighbors, 2) the localized method is compared with SVM from some viewpoints, and 3) approximate nearest neighbors are demonstrated their usefulness in such a localization. As a result, it is shown that SVM is superior to projection learning in many classification problems in its optimal setting but the setting is not easy.