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Efficient semi-supervised learning on locally informative multiple graphs
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DOI：10.1016/j.patcog.2011.08.020.png)
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Abstract
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We address an issue of semi-supervised learning on multiple graphs, over which informative subgraphs are distributed. One application under this setting can be found in molecular biology, where different types of gene networks are generated depending upon experiments. Here an important problem is to annotate unknown genes by using functionally known genes, which connect to unknown genes in gene networks, in which informative parts vary over networks. We present a powerful, time-efficient approach for this problem by combining soft spectral clustering with label propagation for multiple graphs. We demonstrate the effectiveness and efficiency of our approach using both synthetic and real biological datasets. (C) 2011 Elsevier Ltd. All rights reserved.
Keywords:
Semi-supervised learning
Graph integration
Label propagation
Soft spectral clustering
EM (Expectation Maximization) algorithm
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