Analysis of Sparse Representation and Blind Source Separation

By Yuanqing Li, Andrzej S. Cichocki, Shun-ichi Amari. Neural Computation, 2004

325 citations recorded by the source corpus.

Subfields: Computational Mechanics, Signal Processing

View Paper: https://www.semanticscholar.org/paper/b005ec0c615e765889cf457f4f5f0be4454ea261

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In Computational Mechanics

In Signal Processing

Citations in the corpus

Unsupervised Learning of Individuals and Categories from Images (2008, background): “Li, Cichocki, and Amari (2004) discuss the utility of sparse coding applied to this problem, including the aspect that the number of sources is unknown.”
Underdetermined blind source separation based on sparse representation (2006, background): “This problem was preliminarily addressed and discussed in [12].”
Blind Source Separation and Independent Component Analysis : A Review (2004, methodology): “The big advantage of SCA is its ability to reconstruct of original sources even if the number of observations (sensors) is smaller than number of sources under certain weak conditions [101, 46, 65].”
Blind Haze Separation (2006, methodology): “Thus we decided to use ρ = 1 (see also [1, 15, 28, 35]).”
A Fast Approach for Overcomplete Sparse Decomposition Based on Smoothed $\ell ^{0}$ Norm (2008, background): “After [24], the vectors a i , 1 ≤ i ≤ m are called atoms and they collectively form a dictionary over which the signal is to be decomposed.”
Fourth-Order Cumulant-Based Blind Identification of Underdetermined Mixtures (2007, background): “In the case of sparse sources, characterized by Laplacian densities, this can be formulated in terms of a linear programming problem [5], [10], [33].”
Blind Source Separation and Independent Component Analysis: A Review (2005, methodology): “Especially, l0 quasi-norm attract a lot of attention since it ensures sparsest representation [61, 102, 101] .”
Sparse Representation for Signal Classification (2006, methodology): “Research has focused on three aspects of the sparse representation: pursuit methods for solving the optimization problem, such as matching pursuit [1], orthogonal matching pursuit [2], basis pursuit [3], LARS/homotopy methods [4]; design of the dictionary, such as the K-SVD method [5]; the applicati”