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    CVPR
    2008
    IEEE
    198views Computer Vision» more  CVPR 2008»
    Semi-Supervised Discriminant Analysis using robust path-based similarity
    15 years 26 days ago
    Semi-Supervised Discriminant Analysis using robust path-based similarity
    Download  www.cs.ust.hk
    Linear Discriminant Analysis (LDA), which works by maximizing the within-class similarity and minimizing the between-class similarity simultaneously, is a popular dimensionality r...
    Yu Zhang, Dit-Yan Yeung
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    SDM
    2007
    SIAM
    108views Data Mining» more  SDM 2007»
    Semi-Supervised Dimensionality Reduction
    14 years 8 days ago
    Semi-Supervised Dimensionality Reduction
    Download  cs.nju.edu.cn
    Dimensionality reduction is among the keys in mining highdimensional data. This paper studies semi-supervised dimensionality reduction. In this setting, besides abundant unlabeled...
    Daoqiang Zhang, Zhi-Hua Zhou, Songcan Chen
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    VISAPP
    2008
    109views Computer Vision» more  VISAPP 2008»
    Semi-Supervised Dimensionality Reduction Using Pairwise Equivalence Constraints
    14 years 8 days ago
    Semi-Supervised Dimensionality Reduction Using Pairwise Equivalence Constraints
    Download  lear.inrialpes.fr
    Hakan Cevikalp, Jakob J. Verbeek, Fréd&eacu...
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    ICIC
    2009
    Springer
    217views Applied Computing» more  ICIC 2009»
    Dimension Reduction Using Semi-Supervised Locally Linear Embedding for Plant Leaf Classification
    14 years 3 months ago
    Dimension Reduction Using Semi-Supervised Locally Linear Embedding for Plant Leaf Classification
    Download  polyu.academia.edu
    Plant has plenty use in foodstuff, medicine and industry, and is also vitally important for environmental protection. So, it is important and urgent to recognize and classify plant...
    Shanwen Zhang, Kwok-Wing Chau
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    ICDE
    2005
    IEEE
    140views Database» more  ICDE 2005»
    On Discovery of Extremely Low-Dimensional Clusters using Semi-Supervised Projected Clustering
    15 years 6 days ago
    On Discovery of Extremely Low-Dimensional Clusters using Semi-Supervised Projected Clustering
    Download  www.informatik.uni-ulm.de
    Kevin Y. Yip, David W. Cheung, Michael K. Ng
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