Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Blind Source Separation by Sparse Decomposition in a Signal Dictionary
Neural Computation
Generalized Discriminant Analysis Using a Kernel Approach
Neural Computation
Discriminative cluster analysis
ICML '06 Proceedings of the 23rd international conference on Machine learning
Blind separation of disjoint orthogonal signals: demixing N sources from 2 mixtures
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 05
Adaptive dimension reduction using discriminant analysis and K-means clustering
Proceedings of the 24th international conference on Machine learning
K-hyperline clustering learning for sparse component analysis
Signal Processing
Optimality and stability of the K-hyperline clustering algorithm
Pattern Recognition Letters
A novel approach for underdetermined blind sources separation in frequency domain
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
IEEE Transactions on Signal Processing
Blind separation of speech mixtures via time-frequency masking
IEEE Transactions on Signal Processing
Underdetermined blind source separation based on sparse representation
IEEE Transactions on Signal Processing
Blind source separation based on time-frequency signalrepresentations
IEEE Transactions on Signal Processing
A fast mixing matrix estimation method in the wavelet domain
Signal Processing
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Mixing matrix estimation in instantaneous blind source separation (BSS) can be performed by exploiting the sparsity and disjoint orthogonality of source signals. As a result, approaches for estimating the unknown mixing process typically employ clustering algorithms on the mixtures in a parametric domain, where the signals can be sparsely represented. In this paper, we propose two algorithms to perform discriminative clustering of the mixture signals for estimating the mixing matrix. For the case of overdetermined BSS, we develop an algorithm to perform linear discriminant analysis based on similarity measures and combine it with K-hyperline clustering. Furthermore, we propose to perform discriminative clustering in a high-dimensional feature space obtained by an implicit mapping, using the kernel trick, for the case of underdetermined source separation. Using simulations on synthetic data, we demonstrate the improvements in mixing matrix estimation performance obtained using the proposed algorithms in comparison to other clustering methods. Finally we perform mixing matrix estimation from speech mixtures, by clustering single source points in the time-frequency domain, and show that the proposed algorithms achieve higher signal to interference ratio when compared to other baseline algorithms.