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Efficient methods for grouping vectors into low-rank clusters
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DOI：10.1016/j.jcp.2011.03.048.png)
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We present a few practical algorithms for sorting vectors into low-rank clusters. These algorithms rely on a subdivision scheme applied to the space of projections from d-dimensions to 1-dimension. This subdivision scheme can be thought of as a higher-dimensional generalization of quicksort. Given the ability to quickly sort vectors into low-rank clusters, one can efficiently search a matrix for low-rank sub-blocks of large diameter. The ability to detect large-diameter low-rank sub-blocks has many applications, ranging from data-analysis to matrix-compression. (C) 2011 Elsevier Inc. All rights reserved.
Keyword:
Hierarchical factorization
Principal-component-analysis
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3.8
论文数:
1.6W
被引数:
7.4W
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