Concept decompositions for large sparse text data using clustering
Machine Learning
IRBL: An Implicitly Restarted Block-Lanczos Method for Large-Scale Hermitian Eigenproblems
SIAM Journal on Scientific Computing
Document clustering based on non-negative matrix factorization
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Learning sparse features for classification by mixture models
Pattern Recognition Letters
Non-negative Matrix Factorization with Sparseness Constraints
The Journal of Machine Learning Research
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
Two-dimensional non-negative matrix factorization for face representation and recognition
AMFG'05 Proceedings of the Second international conference on Analysis and Modelling of Faces and Gestures
Nonnegative matrix factorization via rank-one downdate
Proceedings of the 25th international conference on Machine learning
A Matrix Factorization Approach for Integrating Multiple Data Views
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Using underapproximations for sparse nonnegative matrix factorization
Pattern Recognition
On the Complexity of Nonnegative Matrix Factorization
SIAM Journal on Optimization
Using population based algorithms for initializing nonnegative matrix factorization
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part II
Subtractive initialization of nonnegative matrix factorizations for document clustering
WILF'11 Proceedings of the 9th international conference on Fuzzy logic and applications
Anderson Acceleration for Fixed-Point Iterations
SIAM Journal on Numerical Analysis
A multilevel approach for nonnegative matrix factorization
Journal of Computational and Applied Mathematics
A careful assessment of recommendation algorithms related to dimension reduction techniques
Knowledge-Based Systems
NIMFA: a python library for nonnegative matrix factorization
The Journal of Machine Learning Research
Review article: Max-margin Non-negative Matrix Factorization
Image and Vision Computing
Efficient Nonnegative Matrix Factorization via projected Newton method
Pattern Recognition
Initialization of nonnegative matrix factorization with vertices of convex polytope
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part I
Regularized nonnegative shared subspace learning
Data Mining and Knowledge Discovery
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
Simulated bidirectional texture functions with silhouette details
Proceedings of Graphics Interface 2013
ADCS reaches adulthood: an analysis of the conference and its community over the last eighteen years
Proceedings of the 18th Australasian Document Computing Symposium
Subtractive clustering for seeding non-negative matrix factorizations
Information Sciences: an International Journal
Comment-based multi-view clustering of web 2.0 items
Proceedings of the 23rd international conference on World wide web
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We describe Nonnegative Double Singular Value Decomposition (NNDSVD), a new method designed to enhance the initialization stage of nonnegative matrix factorization (NMF). NNDSVD can readily be combined with existing NMF algorithms. The basic algorithm contains no randomization and is based on two SVD processes, one approximating the data matrix, the other approximating positive sections of the resulting partial SVD factors utilizing an algebraic property of unit rank matrices. Simple practical variants for NMF with dense factors are described. NNDSVD is also well suited to initialize NMF algorithms with sparse factors. Many numerical examples suggest that NNDSVD leads to rapid reduction of the approximation error of many NMF algorithms.