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- W1539055308 abstract "Graph mining methods enumerate frequent subgraphs efficiently, but they are not necessarily good features for machine learning due to high correlation among features. Thus it makes sense to perform principal component analysis to reduce the dimensionality and create decorrelated features. We present a novel iterative mining algorithm that captures informative patterns corresponding to major entries of top principal components. It repeatedly calls weighted substructure mining where example weights are updated in each iteration. The Lanczos algorithm, a standard algorithm of eigen decomposition, is employed to update the weights. In experiments, our patterns are shown to approximate the principal components obtained by frequent mining." @default.
- W1539055308 created "2016-06-24" @default.
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- W1539055308 date "2008-01-01" @default.
- W1539055308 modified "2023-09-23" @default.
- W1539055308 title "Iterative Subgraph Mining for Principal Component Analysis." @default.
- W1539055308 hasPublicationYear "2008" @default.
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