Convergence of a Sparse Representations Algorithm Applicable to Real or Complex Data. Abstract: Sparse representations has become an important topic in years.
Abstract—Sparse representations has become an important topic in recent years. It consists in representing, say, a signal. (vector) as a linear combination ...
We analyze it thoroughly and show that it converges to the global optimum. We detail the proof in the real case and indicate how to extend it to the complex ...
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On the convergence of FOCUSS algorithm for sparse representation ... Convergence of a Sparse Representations Algorithm Applicable to Real or Complex Data.
Feb 23, 2016 · In this paper, all the data are only real-valued. Suppose that the sample is from space Rd and thus all the samples are concatenated to form a ...
Signals carry overwhelming amounts of data in which relevant information is often more difficult to find than a needle in a haystack.
We show that the LCA has desirable convergence properties, such as stability and global convergence to the optimum of the objective function when it is unique.
ABSTRACT. In this paper we present an algorithm for complex-valued sparse representation. In our previous work we presented an.
The article discusses the proposed implementation of a sparse representation of complex data based on an overcomplete basis, l0/l1 norms, and a neural-like MFNN ...
Feb 20, 2024 · The article presents the results of research into a method for representing complex data based on an overcomplete basis and l0/l1 norms.