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  Partial Least Squares Regression for Graph Mining

Saigo, H., Krämer, N., & Tsuda, K. (2008). Partial Least Squares Regression for Graph Mining. In Y. Li, B. Liu, & S. Sarawagi (Eds.), KDD '08: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 578-586). New York, NY, USA: ACM Press.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-C7BC-7 Version Permalink: http://hdl.handle.net/21.11116/0000-0003-3D39-A
Genre: Conference Paper

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 Creators:
Saigo, H1, 2, Author              
Krämer, N, Author
Tsuda, K1, 2, Author              
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2biological cy, ou_persistent22              

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 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.

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 Dates: 2008-08
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1145/1401890.1401961
BibTex Citekey: 5204
 Degree: -

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Title: 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Place of Event: Las Vegas, NV, USA
Start-/End Date: 2008-08-24 - 2008-08-27

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Title: KDD '08: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Source Genre: Proceedings
 Creator(s):
Li, Y, Editor
Liu, B, Editor
Sarawagi, S, Editor
Affiliations:
-
Publ. Info: New York, NY, USA : ACM Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 578 - 586 Identifier: ISBN: 978-1-60558-193-4