Partial least squares regression for graph mining

9 years 2 months ago
Partial least squares regression for graph mining
Attributed graphs are increasingly more common in many application domains such as chemistry, biology and text processing. A central issue in graph mining is how to collect informative subgraph patterns for a given learning task. We propose an iterative mining method based on partial least squares regression (PLS). To apply PLS to graph data, a sparse version of PLS is developed first and then it is combined with a weighted pattern mining algorithm. The mining algorithm is iteratively called with different weight vectors, creating one latent component per one mining call. Our method, graph PLS, is efficient and easy to implement, because the weight vector is updated with elementary matrix calculations. In experiments, our graph PLS algorithm showed competitive prediction accuracies in many chemical datasets and its efficiency was significantly superior to graph boosting (gBoost) and the naive method based on frequent graph mining. Categories and Subject Descriptors I.5.2 [Pattern Reco...
Hiroto Saigo, Koji Tsuda, Nicole Krämer
Added 30 Nov 2009
Updated 30 Nov 2009
Type Conference
Year 2008
Where KDD
Authors Hiroto Saigo, Koji Tsuda, Nicole Krämer
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