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k-means discriminant maps for data visualization and classification

Published: 16 March 2008 Publication History

Abstract

Over the years, many dimensionality reduction algorithms have been proposed for learning the structure of high dimensional data by linearly or non-linearly transforming it into a low-dimensional space. Some techniques can keep the local structure of data, while the others try to preserve the global structure. In this paper, we propose a linear dimensionality reduction technique that characterizes the local and global properties of data by firstly applying k-means algorithm on original data, and then finding the projection by simultaneously globally maximizing the between-cluster scatter matrix and locally minimizing the within-cluster scatter matrix, which actually keeps both local and global structure of data. Low complexity and structure preserving are two main advantages of the proposed technique. The experiments on both artificial and real data sets show the effectiveness and novelty of proposed algorithm in visualization and classification tasks.

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    cover image ACM Conferences
    SAC '08: Proceedings of the 2008 ACM symposium on Applied computing
    March 2008
    2586 pages
    ISBN:9781595937537
    DOI:10.1145/1363686
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    Publication History

    Published: 16 March 2008

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    Author Tags

    1. dimensionality reduction
    2. k-means
    3. linear discriminant analysis
    4. manifold learning

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    SAC '08: The 2008 ACM Symposium on Applied Computing
    March 16 - 20, 2008
    Fortaleza, Ceara, Brazil

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