2008 PartialLeastSquaresRegressionfo

• (Saigo et al., 2008) ⇒ Hiroto Saigo, Nicole Krämer, and Koji Tsuda. (2008). “Partial Least Squares Regression for Graph Mining.” In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2008). doi:10.1145/1401890.1401961

Subject Headings: Partial Least Squares Regression.

Cited By

• http://scholar.google.com/scholar?q=%22Partial+least+squares+regression+for+graph+mining%22+2008
• http://portal.acm.org/citation.cfm?doid=1401890.1401961&preflayout=flat#citedby

Quotes

Abstract

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.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2008 PartialLeastSquaresRegressionfo	Hiroto Saigo Nicole Krämer Koji Tsuda			Partial Least Squares Regression for Graph Mining		KDD-2008 Proceedings		10.1145/1401890.1401961		2008