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  • Classifying microarray cancer datasets using nearest subspace classification

    Author(s)
    Cohen, Michael
    Paliwal, Kuldip
    Griffith University Author(s)
    Paliwal, Kuldip K.
    Cohen, Michael
    Year published
    2008
    Metadata
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    Abstract
    In this paper we implement and test the recently described nearest subspace classifier on a range of microarray cancer datasets. Its classification accuracy is tested against nearest neighbor and nearest centroid algorithms, and is shown to give a significant improvement. This classification system uses class-dependent PCA to construct a subspace for each class. Test vectors are assigned the class label of the nearest subspace, which is defined as the minimum reconstruction error across all subspaces. Furthermore, we demonstrate this distance measure is equivalent to the null-space component of the vector being analyzed.In this paper we implement and test the recently described nearest subspace classifier on a range of microarray cancer datasets. Its classification accuracy is tested against nearest neighbor and nearest centroid algorithms, and is shown to give a significant improvement. This classification system uses class-dependent PCA to construct a subspace for each class. Test vectors are assigned the class label of the nearest subspace, which is defined as the minimum reconstruction error across all subspaces. Furthermore, we demonstrate this distance measure is equivalent to the null-space component of the vector being analyzed.
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    Conference Title
    Supplementary Proceedings [of the] Third IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB 2008)
    Publisher URI
    https://www.springer.com/gp/book/9783540884347
    Subject
    Pattern Recognition and Data Mining
    Publication URI
    http://hdl.handle.net/10072/24328
    Collection
    • Conference outputs

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