A comparison of typical ℓp minimization algorithms
Neurocomputing（2013）
摘要
Recently, compressed sensing has been widely applied to various areas such as signal processing, machine learning, and pattern recognition. To find the sparse representation of a vector w.r.t. a dictionary, an @?"1 minimization problem, which is convex, is usually solved in order to overcome the computational difficulty. However, to guarantee that the @?"1 minimizer is close to the sparsest solution, strong incoherence conditions should be imposed. In comparison, nonconvex minimization problems such as those with the @?"p(0
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关键词
minimization problem,nonconvex minimization problem,computational difficulty,machine learning,pattern recognition,signal processing,sparse representation,sparsest solution,strong incoherence condition,various area,p minimization algorithm
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