Dictionary learning algorithms for sparse representation
Neural Computation
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Blind separation of positive sources by globally convergent gradient search
Neural Computation
Non-negative Matrix Factorization with Sparseness Constraints
The Journal of Machine Learning Research
Sparse Image Coding Using a 3D Non-Negative Tensor Factorization
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Learning Sparse Overcomplete Codes for Images
Journal of VLSI Signal Processing Systems
Controlling sparseness in non-negative tensor factorization
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
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
A Parallel Nonnegative Tensor Factorization Algorithm for Mining Global Climate Data
ICCS 2009 Proceedings of the 9th International Conference on Computational Science
A compressive sensing approach for progressive transmission of images
DSP'09 Proceedings of the 16th international conference on Digital Signal Processing
Using underapproximations for sparse nonnegative matrix factorization
Pattern Recognition
A multilevel approach for nonnegative matrix factorization
Journal of Computational and Applied Mathematics
Multidimensional Systems and Signal Processing
Fast Nonnegative Matrix Factorization: An Active-Set-Like Method and Comparisons
SIAM Journal on Scientific Computing
Nonnegative dictionary learning by nonnegative matrix factorization with a sparsity constraint
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part II
Linked PARAFAC/CP tensor decomposition and its fast implementation for multi-block tensor analysis
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
Multi-class learning from class proportions
Neurocomputing
Non-negative multiple matrix factorization
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Journal of Global Optimization
Hi-index | 0.00 |
In the paper we present new Alternating Least Squares (ALS) algorithms for Nonnegative Matrix Factorization (NMF) and their extensions to 3D Nonnegative Tensor Factorization (NTF) that are robust in the presence of noise and have many potential applications, including multi-way Blind Source Separation (BSS), multi-sensory or multi-dimensional data analysis, and nonnegative neural sparse coding. We propose to use local cost functions whose simultaneous or sequential (one by one) minimization leads to a very simple ALS algorithm which works under some sparsity constraints both for an under-determined (a system which has less sensors than sources) and overdetermined model. The extensive experimental results confirm the validity and high performance of the developed algorithms, especially with usage of the multi-layer hierarchical NMF. Extension of the proposed algorithm to multidimensional Sparse Component Analysis and Smooth Component Analysis is also proposed.