Principal Direction Divisive Partitioning
Data Mining and Knowledge Discovery
Information-theoretic co-clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Nonorthogonal decomposition of binary matrices for bounded-error data compression and analysis
ACM Transactions on Mathematical Software (TOMS)
Orthogonal nonnegative matrix t-factorizations for clustering
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
The Relationships Among Various Nonnegative Matrix Factorization Methods for Clustering
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Binary Matrix Factorization with Applications
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Nonnegative Matrix Factorization on Orthogonal Subspace
Pattern Recognition Letters
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Orthogonal Nonnegative Matrix Tri-Factorization (ONMTF), a dimension reduction method using three small matrices to approximate an input data matrix, clusters the rows and columns of an input data matrix simultaneously However, ONMTF is computationally expensive due to an intensive computation of the Lagrangian multipliers for the orthogonal constraints In this paper, we introduce Fast Orthogonal Nonnegative Matrix Tri-Factorization (FONT), which uses approximate constants instead of computing the Lagrangian multipliers As a result, FONT reduces the computational complexity significantly Experiments on document datasets show that FONT outperforms ONMTF in terms of clustering quality and running time Moreover, FONT is further accelerated by incorporating Alternating Least Squares, and can be much faster than ONMTF.