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    CVPR
    2006
    IEEE
    257views Computer Vision» more  CVPR 2006»
    Unsupervised Learning of Categories from Sets of Partially Matching Image Features
    14 years 12 months ago
    Unsupervised Learning of Categories from Sets of Partially Matching Image Features
    Download  vision.jhu.edu
    We present a method to automatically learn object categories from unlabeled images. Each image is represented by an unordered set of local features, and all sets are embedded into...
    Kristen Grauman, Trevor Darrell
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    JCST
    2008
    121views more  JCST 2008»
    Clustering Text Data Streams
    13 years 9 months ago
    Clustering Text Data Streams
    Download  www.cse.cuhk.edu.hk
    Abstract Clustering text data streams is an important issue in data mining community and has a number of applications such as news group filtering, text crawling, document organiza...
    Yubao Liu, Jiarong Cai, Jian Yin, Ada Wai-Chee Fu
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    SDM
    2012
    SIAM
    216views Data Mining» more  SDM 2012»
    Feature Selection "Tomography" - Illustrating that Optimal Feature Filtering is Hopelessly Ungeneralizable
    12 years 7 days ago
    Feature Selection "Tomography" - Illustrating that Optimal Feature Filtering is Hopelessly Ungeneralizable
    Download  www.hpl.hp.com
    :  Feature Selection “Tomography” - Illustrating that Optimal Feature Filtering is Hopelessly Ungeneralizable George Forman HP Laboratories HPL-2010-19R1 Feature selection; ...
    George Forman
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    SDM
    2008
    SIAM
    136views Data Mining» more  SDM 2008»
    Exploration and Reduction of the Feature Space by Hierarchical Clustering
    13 years 11 months ago
    Exploration and Reduction of the Feature Space by Hierarchical Clustering
    Download  www.di.unito.it
    In this paper we propose and test the use of hierarchical clustering for feature selection. The clustering method is Ward's with a distance measure based on GoodmanKruskal ta...
    Dino Ienco, Rosa Meo
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    IPM
    2006
    151views more  IPM 2006»
    Document clustering using nonnegative matrix factorization
    13 years 9 months ago
    Document clustering using nonnegative matrix factorization
    Download  www.math.wfu.edu
    A methodology for automatically identifying and clustering semantic features or topics in a heterogeneous text collection is presented. Textual data is encoded using a low rank no...
    Farial Shahnaz, Michael W. Berry, V. Paul Pauca, R...
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