Learning to Categorize Objects Using Temporal Coherence
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Blind source separation using the maximum signal fraction approach
Signal Processing
Topic Identification in Dynamical Text by Complexity Pursuit
Neural Processing Letters
An Adaptive Hierarchical Model of the Ventral Visual Pathway Implemented on a Mobile Robot
BMCV '02 Proceedings of the Second International Workshop on Biologically Motivated Computer Vision
A Note on Stone's Conjecture of Blind Signal Separation
Neural Computation
An Efficient Measure of Signal Temporal Predictability for Blind Source Separation
Neural Processing Letters
A fixed-point algorithm for blind source separation with nonlinear autocorrelation
Journal of Computational and Applied Mathematics
Blind source separation with nonlinear autocorrelation and non-Gaussianity
Journal of Computational and Applied Mathematics
Fast nonlinear autocorrelation algorithm for source separation
Pattern Recognition
A PDF-Matched Modification to Stone's Measure of Predictability for Blind Source Separation
ISNN '09 Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks
Theoretical background for ensemble methods with multivariate decomposition
SMO'09 Proceedings of the 9th WSEAS international conference on Simulation, modelling and optimization
dAMUSE---A new tool for denoising and blind source separation
Digital Signal Processing
The generalized eigendecomposition approach to the blind source separation problem
Digital Signal Processing
On blind separability based on the temporal predictability method
Neural Computation
A complexity constrained nonnegative matrix factorization for hyperspectral unmixing
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Smooth component analysis as ensemble method for prediction improvement
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Blind signal deconvolution as an instantaneous blind separation of statistically dependent sources
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Research of blind images separation algorithm based on Kernel space
ICNC'09 Proceedings of the 5th international conference on Natural computation
Blind source separation with low frequency compensation for convolutive mixtures
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
Noise detection for ensemble methods
ICAISC'10 Proceedings of the 10th international conference on Artificial intelligence and soft computing: Part I
Blind Source Separation Using Quadratic form Innovation
Neural Processing Letters
Independent component analysis using bregman divergences
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part II
A learning framework for blind source separation using generalized eigenvalues
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
Multistage covariance approach to measure the randomness in financial time series analysis
KES-AMSTA'11 Proceedings of the 5th KES international conference on Agent and multi-agent systems: technologies and applications
Regularized sparse Kernel slow feature analysis
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
Maximum contrast analysis for nonnegative blind source separation
Computers & Mathematics with Applications
Prediction improvement via smooth component analysis and neural network mixing
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
A novel unified SPM-ICA-PCA method for detecting epileptic activities in resting-state fMRI
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part II
Blind separation of digital signal sources in noise circumstance
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
Hybrid linear and nonlinear complexity pursuit for blind source separation
Journal of Computational and Applied Mathematics
Smooth component analysis and MSE decomposition for ensemble methods
KES-AMSTA'12 Proceedings of the 6th KES international conference on Agent and Multi-Agent Systems: technologies and applications
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A measure of temporal predictability is defined and used to separate linear mixtures of signals. Given any set of statistically independent source signals, it is conjectured here that a linear mixture of those signals has the following property: the temporal predictability of any signal mixture is less than (or equal to) that of any of its component source signals. It is shown that this property can be used to recover source signals from a set of linear mixtures of those signals by finding an un-mixing matrix that maximizes a measure of temporal predictability for each recovered signal. This matrix is obtained as the solution to a generalized eigenvalue problem; such problems have scaling characteristics of O(N3), where N is the number of signal mixtures. In contrast to independent component analysis, the temporal predictability method requires minimal assumptions regarding the probability density functions of source signals. It is demonstrated that the method can separate signal mixtures in which each mixture is a linear combination of source signals with supergaussian, subgaussian, and gaussian probability density functions and on mixtures of voices and music.