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
Nonnegative features of spectro-temporal sounds for classification
Pattern Recognition Letters
Document clustering using nonnegative matrix factorization
Information Processing and Management: an International Journal
A Generalized Divergence Measure for Nonnegative Matrix Factorization
Neural Computation
Integration of Stochastic Models by Minimizing α-Divergence
Neural Computation
Nonnegative matrix factorization for motor imagery EEG classification
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
Csiszár’s divergences for non-negative matrix factorization: family of new algorithms
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Monaural music source separation: nonnegativity, sparseness, and shift-invariance
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Extended SMART algorithms for non-negative matrix factorization
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
Non-negative matrix factorization with quasi-newton optimization
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
On the Convergence of Multiplicative Update Algorithms for Nonnegative Matrix Factorization
IEEE Transactions on Neural Networks
Projective Nonnegative Matrix Factorization with α -Divergence
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
Weighted Nonnegative Matrix Co-Tri-Factorization for Collaborative Prediction
ACML '09 Proceedings of the 1st Asian Conference on Machine Learning: Advances in Machine Learning
DSP'09 Proceedings of the 16th international conference on Digital Signal Processing
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
Divergence-based vector quantization
Neural Computation
Anechoic Blind Source Separation Using Wigner Marginals
The Journal of Machine Learning Research
Algorithms for nonnegative matrix factorization with the β-divergence
Neural Computation
Quadratic nonnegative matrix factorization
Pattern Recognition
Sparse nonnegative matrix factorization with ℓ0-constraints
Neurocomputing
Three penalized EM-type algorithms for PET image reconstruction
Computers in Biology and Medicine
Multistability of α-divergence based NMF algorithms
Computers & Mathematics with Applications
Pairwise clustering with t-PLSI
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
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Non-negative matrix factorization (NMF) is a popular technique for pattern recognition, data analysis, and dimensionality reduction, the goal of which is to decompose non-negative data matrix X into a product of basis matrix A and encoding variable matrix S with both A and S allowed to have only non-negative elements. In this paper, we consider Amari's @a-divergence as a discrepancy measure and rigorously derive a multiplicative updating algorithm (proposed in our recent work) which iteratively minimizes the @a-divergence between X and AS. We analyze and prove the monotonic convergence of the algorithm using auxiliary functions. In addition, we show that the same algorithm can be also derived using Karush-Kuhn-Tucker (KKT) conditions as well as the projected gradient. We provide two empirical study for image denoising and EEG classification, showing the interesting and useful behavior of the algorithm in cases where different values of @a (@a=0.5,1,2) are used.