Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Document clustering based on non-negative matrix factorization
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Non-negative matrix factorization based methods for object recognition
Pattern Recognition Letters
Generative model-based document clustering: a comparative study
Knowledge and Information Systems
Matching Theory (North-Holland mathematics studies)
Matching Theory (North-Holland mathematics studies)
Nonlinear programming: a historical view
ACM SIGMAP Bulletin
Document clustering using nonnegative matrix factorization
Information Processing and Management: an International Journal
Orthogonal nonnegative matrix t-factorizations for clustering
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Journal of VLSI Signal Processing Systems
Top 10 algorithms in data mining
Knowledge and Information Systems
Clustering based on matrix approximation: a unifying view
Knowledge and Information Systems
Non-negative matrix factorization for semi-supervised data clustering
Knowledge and Information Systems
Characterization and evaluation of similarity measures for pairs of clusterings
Knowledge and Information Systems
Fast orthogonal nonnegative matrix tri-factorization for simultaneous clustering
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Nonnegative matrix factorization on orthogonal subspace with smoothed l0 norm constrained
IScIDE'12 Proceedings of the third Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Convergent Projective Non-negative Matrix Factorization with Kullback-Leibler Divergence
Pattern Recognition Letters
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Nonnegative Matrix Factorization (NMF), a parts-based representation using two small factor matrices to approximate an input data matrix, has been widely used in data mining, pattern recognition and signal processing. Orthogonal NMF which imposes orthogonality constraints on the factor matrices can improve clustering performance. However, the existing orthogonal NMF algorithms are either computationally expensive or have to incorporate prior information to achieve orthogonality. In our research, we propose an algorithm called Nonnegative Matrix Factorization on Orthogonal Subspace (NMFOS), in which the generation of orthogonal factor matrices is part of objective function minimization. Thus, orthogonality is achieved without resorting to additional constraints, and the computational complexity is decreased. We develop two algorithms based on the Euclidean distance metric and the generalized Kullback-Leibler divergence, respectively. Experiments on 10 document datasets show that NMFOS improves clustering accuracy. On a facial image database, NMFOS achieves a better parts-based representation with a significant reduction in computational complexity.