Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
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
Smooth functions and local extreme values
Computational Statistics & Data Analysis
Controlling sparseness in non-negative tensor factorization
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Why Simple Shrinkage Is Still Relevant for Redundant Representations?
IEEE Transactions on Information Theory
Morphological Component Analysis: An Adaptive Thresholding Strategy
IEEE Transactions on Image Processing
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In the paper, we present a new approach to multi-way Blind Source Separation (BSS) and corresponding 3D tensor factorization that has many potential applications in neuroscience and multi-sensory or multidimensional data analysis, and neural sparse coding. We propose to use a set of local cost functions with flexible penalty and regularization terms whose simultaneous or sequential (one by one) minimization via a projected gradient technique leads to simple Hebbian-like local algorithms that work well not only for an over-determined case but also (under some weak conditions) for an under-determined case (i.e., a system which has less sensors than sources). The experimental results confirm the validity and high performance of the developed algorithms, especially with usage of the multi-layer hierarchical approach.