2013 FastRank2NonnegativeMatrixFacto

• (Kuang & Park, 2013) ⇒ Da Kuang, and Haesun Park. (2013). “Fast Rank-2 Nonnegative Matrix Factorization for Hierarchical Document Clustering.” In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ISBN:978-1-4503-2174-7 doi:10.1145/2487575.2487606

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222013%22+Fast+Rank-2+Nonnegative+Matrix+Factorization+for+Hierarchical+Document+Clustering
• http://dl.acm.org/citation.cfm?id=2487575.2487606&preflayout=flat#citedby

Quotes

Author Keywords

• Active-set algorithm; clustering; hierarchical document clustering; nonnegative matrix factorization; rank-2 nmf

Abstract

Nonnegative matrix factorization (NMF) has been successfully used as a clustering method especially for flat partitioning of documents. In this paper, we propose an efficient hierarchical document clustering method based on a new algorithm for rank-2 NMF. When the two block coordinate descent framework of nonnegative least squares is applied to computing rank-2 NMF, each subproblem requires a solution for nonnegative least squares with only two columns in the matrix. We design the algorithm for rank-2 NMF by exploiting the fact that an exhaustive search for the optimal active set can be performed extremely fast when solving these NNLS problems. In addition, we design a measure based on the results of rank-2 NMF for determining which leaf node should be further split. On a number of text data sets, our proposed method produces high-quality tree structures in significantly less time compared to other methods such as hierarchical K-means, standard NMF, and latent Dirichlet allocation.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2013 FastRank2NonnegativeMatrixFacto	Haesun Park Da Kuang			Fast Rank-2 Nonnegative Matrix Factorization for Hierarchical Document Clustering				10.1145/2487575.2487606		2013