摘要: Relational learning has attracted more and attention in recent years due to its phenomenal impact various important applications which involve multi-type interrelated data objects, such as bioinformatics, citation analysis, epidemiology web analytics. However, the research on unsupervised relational is still limited preliminary. In this paper, we propose a general model, collective factorization related matrices, for clustering. The model applicable with structures. Under proposed specific distance function ‐ Euclidean function, derive novel spectral clustering algorithm, clustering, cluster objects simultaneously. algorithm iteratively embeds each type of into low dimensional spaces benefits from interactions among hidden structures different types objects. Extensive experiments demonstrate promise effectiveness algorithm.
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Xiaofeng He, Chris Ding, Rong Jin, Horst D. Simon, On the Equivalence of Nonnegative Matrix Factorization and K-means - Spectral Clustering Lawrence Berkeley National Laboratory. ,(2005)
Thomas Hofmann, Probabilistic latent semantic analysis uncertainty in artificial intelligence. ,vol. 15, pp. 289- 296 ,(1999)
Ran El-Yaniv, Oren Souroujon, Iterative Double Clustering for Unsupervised and Semi-Supervised Learning neural information processing systems. pp. 1025- 1032 ,(2001) , 10.1007/3-540-44795-4_11
Thomas Hofmann, Jan Puzicha, Latent class models for collaborative filtering international joint conference on artificial intelligence. pp. 688- 693 ,(1999)
Eran Segal, Daphne Koller, Ben Taskar, Probabilistic classification and clustering in relational data international joint conference on artificial intelligence. pp. 870- 876 ,(2001)
Daniel D. Lee, H. Sebastian Seung, Learning the parts of objects by non-negative matrix factorization Nature. ,vol. 401, pp. 788- 791 ,(1999) , 10.1038/44565
Srujana Merugu, Inderjit Dhillon, Arindam Banerjee, Joydeep Ghosh, Dharmendra S. Modha, A Generalized Maximum Entropy Approach to Bregman Co-clustering and Matrix Approximation Journal of Machine Learning Research. ,vol. 8, pp. 1919- 1986 ,(2007) , 10.5555/1314498.1314563
Joydeep Ghosh, Alexander Strehl, Cluster ensembles: a knowledge reuse framework for combining partitionings national conference on artificial intelligence. pp. 93- 98 ,(2002) , 10.5555/777092.777110
Hongyuan Zha, Xiaofeng He, Chris Ding, Horst Simon, Ming Gu, Bipartite graph partitioning and data clustering Proceedings of the tenth international conference on Information and knowledge management - CIKM'01. pp. 25- 32 ,(2001) , 10.1145/502585.502591
Chris Ding, Xiaofeng He, Linearized cluster assignment via spectral ordering Twenty-first international conference on Machine learning - ICML '04. pp. 30- ,(2004) , 10.1145/1015330.1015407