2009 CoClusteringOnManifolds

• (Gu et al., 2009) ⇒ Quanquan Gu, and Jie Zhou. (2009). “Co-clustering on Manifolds.” In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2009). doi:10.1145/1557019.1557063

Subject Headings:

Notes

• Categories and Subject Descriptors: I.2.6 Artificial Intelligence: Learning; I.5.3 Pattern Recognition: Clustering.
• General Terms: Algorithms, Experimentations

Cited By

• http://scholar.google.com/scholar?q=%22Co-clustering+on+manifolds%22+2009
• http://portal.acm.org/citation.cfm?doid=1557019.1557063&preflayout=flat#citedby

Quotes

Author Keywords

Co-clustering, Data Manifold, Feature Manifold, Graph Regularization, Semi-nonnegative matrix tri-factorization

Abstract

Co-clustering is based on the duality between data points (e.g. documents) and features (e.g. words), i.e. data points can be grouped based on their distribution on features, while features can be grouped based on their distribution on the data points. In the past decade, several co-clustering algorithms have been propose and shown to be superior to traditional one-side clustering. However, existing co-clustering algorithms fail to consider the geometric structure in the data, which is essential for clustering data on manifold. To address this problem, in this paper, we propose a Dual Regularized Co-Clustering (DRCC) method based on semi-nonnegative matrix tri-factorization. We deem that not only the data points, but also the features are sampled from some manifolds, namely data manifold and feature manifold respectively. As a result, we construct two graphs, i.e. data graph and feature graph, to explore the geometric structure of data manifold and feature manifold. Then our co-clustering method is formulated as semi-nonnegative matrix tri-factorization with two graph regularizers, requiring that the cluster labels of data points are smooth with respect to the data manifold, while the cluster labels of features are smooth with respect to the feature manifold. We will show that DRCC can be solved via alternating minimization, and its convergence is theoretically guaranteed. Experiments of clustering on many benchmark data sets demonstrate that the propose method outperforms many state of the art clustering methods.

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References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2009 CoClusteringOnManifolds	Quanquan Gu Jie Zhou			Co-clustering on Manifolds		KDD-2009 Proceedings		10.1145/1557019.1557063		2009