Space or time adaptive signal processing by neural network models
AIP Conference Proceedings 151 on Neural Networks for Computing
Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Jacobi Angles for Simultaneous Diagonalization
SIAM Journal on Matrix Analysis and Applications
A fast fixed-point algorithm for independent component analysis
Neural Computation
Atomic Decomposition by Basis Pursuit
SIAM Journal on Scientific Computing
Journal of Global Optimization
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A new concept for separability problems in blind source separation
Neural Computation
Non-negative Matrix Factorization with Sparseness Constraints
The Journal of Machine Learning Research
Blind Source Separation by Sparse Decomposition in a Signal Dictionary
Neural Computation
Learning Overcomplete Representations
Neural Computation
IEEE Transactions on Signal Processing
A "nonnegative PCA" algorithm for independent component analysis
IEEE Transactions on Neural Networks
Sparse component analysis and blind source separation of underdetermined mixtures
IEEE Transactions on Neural Networks
Robust sparse component analysis based on a generalized Hough transform
EURASIP Journal on Applied Signal Processing
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The decomposition of surface electromyogram data sets (s-EMG) is studied using blind source separation techniques based on sparseness; namely independent component analysis, sparse nonnegative matrix factorization, and sparse component analysis. When applied to artificial signals we find noticeable differences of algorithm performance depending on the source assumptions. In particular, sparse nonnegative matrix factorization outperforms the other methods with regard to increasing additive noise. However, in the case of real s-EMG signals we show that despite the fundamental differences in the various models, the methods yield rather similar results and can successfully separate the source signal. This can be explained by the fact that the different sparseness assumptions (super-Gaussianity, positivity together with minimal 1-norm and fixed number of zeros, respectively) are all only approximately fulfilled thus apparently forcing the algorithms to reach similar results, but from different initial conditions.