Subclass-oriented Dimension Reduction with constraint transformation and manifold regularization

Bin Tong, Einoshin Suzuki

研究成果: 書籍/レポート タイプへの寄稿会議への寄与

1 被引用数 (Scopus)

抄録

We propose a new method, called Subclass-oriented Dimension Reduction with Pairwise Constraints (SODRPaC), for dimension reduction on high dimensional data. Current linear semi-supervised dimension reduction methods using pairwise constraints, e.g., must-link constraints and cannot-link constraints, can not handle appropriately the data of multiple subclasses where the points of a class are separately distributed in different groups. To illustrate this problem, wparticularly classify the must-link constraint into two categories, which are theinter-subclass must-link constraint and the intra-subclass must-link constraint, respectively. We argue that handling the inter-subclass must-link constraint is challenging for current discriminant criteria. Inspired by the above observation and the cluster assumption that nearby points are possible in the same class, we carefully transform must-link constraints into cannot-link constraints, and then propose a new discriminant criterion by employing the cannot-link constraints and the compactness of shared nearest neighbors. For the reason that the local data structure is one of the most significant features for the data of multiple subclasses, manifold regularization is also incorporated in our dimension reduction framework. Extensive experiments on both synthetic and practical data sets illustrate the effectiveness of our method.

本文言語英語
ホスト出版物のタイトルAdvances in Knowledge Discovery and Data Mining - 14th Pacific-Asia Conference, PAKDD 2010, Proceedings
ページ1-13
ページ数13
PART 2
DOI
出版ステータス出版済み - 2010
イベント14th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2010 - Hyderabad, インド
継続期間: 6月 21 20106月 24 2010

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
番号PART 2
6119 LNAI
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

その他

その他14th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2010
国/地域インド
CityHyderabad
Period6/21/106/24/10

!!!All Science Journal Classification (ASJC) codes

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)

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