Regularized clustering for documents
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Knowledge transformation from word space to document space
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Data Clustering with Semi-binary Nonnegative Matrix Factorization
ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
Computational Intelligence and Neuroscience - Advances in Nonnegative Matrix and Tensor Factorization
Non-negative matrix factorization for semi-supervised data clustering
Knowledge and Information Systems
Incremental subspace learning via non-negative matrix factorization
Pattern Recognition
Proceedings of the eleventh international joint conference on Measurement and modeling of computer systems
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Weighted Nonnegative Matrix Co-Tri-Factorization for Collaborative Prediction
ACML '09 Proceedings of the 1st Asian Conference on Machine Learning: Advances in Machine Learning
Document Clustering with Cluster Refinement and Non-negative Matrix Factorization
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
Information Processing and Management: an International Journal
Linear and nonlinear projective nonnegative matrix factorization
IEEE Transactions on Neural Networks
Feature subset non-negative matrix factorization and its applications to document understanding
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Improved MinMax cut graph clustering with nonnegative relaxation
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part II
Uncovering transcriptional regulatory networks by sparse Bayesian factor model
EURASIP Journal on Advances in Signal Processing - Special issue on genomic signal processing
Document clustering using NMF and fuzzy relation
Proceedings of the 5th International Conference on Ubiquitous Information Management and Communication
Hierarchical visual event pattern mining and its applications
Data Mining and Knowledge Discovery
Integrating Document Clustering and Multidocument Summarization
ACM Transactions on Knowledge Discovery from Data (TKDD)
DClusterE: A Framework for Evaluating and Understanding Document Clustering Using Visualization
ACM Transactions on Intelligent Systems and Technology (TIST)
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
On trivial solution and scale transfer problems in graph regularized NMF
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Comparative document summarization via discriminative sentence selection
ACM Transactions on Knowledge Discovery from Data (TKDD)
SumView: A Web-based engine for summarizing product reviews and customer opinions
Expert Systems with Applications: An International Journal
Comparative Document Summarization via Discriminative Sentence Selection
ACM Transactions on Knowledge Discovery from Data (TKDD)
Spatially correlated nonnegative matrix factorization for image analysis
IScIDE'12 Proceedings of the third Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Guided learning for role discovery (GLRD): framework, algorithms, and applications
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Subtractive clustering for seeding non-negative matrix factorizations
Information Sciences: an International Journal
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The nonnegative matrix factorization (NMF) has been shown recently to be useful for clustering and various extensions and variations of NMF have been proposed recently. Despite significant research progress in this area, few attempts have been made to establish the connections between various factorization methods while highlighting their differences. In this paper we aim to provide a comprehensive study on matrix factorization for clustering. In particular, we present an overview and summary on various matrix factorization algorithms and theoretically analyze the relationships among them. Experiments are also conducted to empirically evaluate and compare various factorization methods. In addition, our study also answers several previously unaddressed yet important questions for matrix factorizations including the interpretation and normalization of cluster posterior and the benefits and evaluation of simultaneous clustering. We expect our study would provide good insights on matrix factorization research for clustering.