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ICONIP
2007

Flexible Component Analysis for Sparse, Smooth, Nonnegative Coding or Representation

8 years 8 months ago
Flexible Component Analysis for Sparse, Smooth, Nonnegative Coding or Representation
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.
Andrzej Cichocki, Anh Huy Phan, Rafal Zdunek, Liqi
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2007
Where ICONIP
Authors Andrzej Cichocki, Anh Huy Phan, Rafal Zdunek, Liqing Zhang
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