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 with Sparseness Constraints
The Journal of Machine Learning Research
On the Low-Rank Approximation of Data on the Unit Sphere
SIAM Journal on Matrix Analysis and Applications
Email Surveillance Using Non-negative Matrix Factorization
Computational & Mathematical Organization Theory
The Relationships Among Various Nonnegative Matrix Factorization Methods for Clustering
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Initialization enhancer for non-negative matrix factorization
Engineering Applications of Artificial Intelligence
SVD based initialization: A head start for nonnegative matrix factorization
Pattern Recognition
Gene tree labeling using nonnegative matrix factorization on biomedical literature
Computational Intelligence and Neuroscience - Advances in Nonnegative Matrix and Tensor Factorization
Document clustering using nonnegative matrix factorization
Information Processing and Management: an International Journal
Nonnegative Matrix and Tensor Factorizations: Applications to Exploratory Multi-way Data Analysis and Blind Source Separation
Information Processing and Management: an International Journal
Non-Negative Matrix Factorization for Semisupervised Heterogeneous Data Coclustering
IEEE Transactions on Knowledge and Data Engineering
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
Sparse nonnegative matrix factorization applied to microarray data sets
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
| Hi-index | 0.07 |
Non-negative matrix factorization is a multivariate analysis method which is proven to be useful in many areas such as bio-informatics, molecular pattern discovery, pattern recognition, document clustering and so on. It seeks a reduced representation of a multivariate data matrix into the product of basis and encoding matrices possessing only non-negative elements, in order to learn the so called part-based representations of data. All algorithms for computing non-negative matrix factorization are iterative, therefore particular emphasis must be placed on a proper initialization of NMF because of its local convergence. The problem of selecting appropriate starting matrices becomes more complex when data possess special meaning as in document clustering. In this paper, we propose the adoption of the subtractive clustering algorithm as a scheme to generate initial matrices for non-negative matrix factorization algorithms. Comparisons with other commonly adopted initializations of non-negative matrix factorization algorithms have been performed and the proposed scheme reveals to be a good trade-off between effectiveness and speed. Moreover, the effectiveness of the proposed initialization to suggest a number of basis for NMF, when data distances are estimated, is illustrated when NMF is used for solving clustering problems where the number of groups in which the data are grouped is not known a priori. The influence of a proper rank factor on the interpretability and the effectiveness of the results are also discussed.