PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Partial Least Squares Regression for Graph Mining
Hiroto Saigo, Nicole Krämer and Koji Tsuda
In: 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD2008), 24-27 Aug 2008, Las Vegas, USA.


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.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:4175
Deposited By:Nicole Krämer
Deposited On:18 October 2008