منابع مشابه
Sparse BSS in the presence of outliers
While real-world data are often grossly corrupted, most techniques of blind source separation (BSS) give erroneous results in the presence of outliers. We propose a robust algorithm that jointly estimates the sparse sources and outliers without requiring any prior knowledge on the outliers. More precisely, it uses an alternative weighted scheme to weaken the influence of the estimated outliers....
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We investigate the use of sparse priors to regularize nonnegative blind source separation (BSS) problems. Dealing with the nonnegativity constraint in the direct/sample domain, and at the same time sparsity in some other signal representation, raises algorithmic issues. We show how such sparse non-negative BSS problems can be tackled with the help of proximal splitting methods. We present a pre...
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A novel theory of sparse recovery is presented in order to bridge the standard compressive sensing framework and the one-bit compressive sensing framework. In the former setting, sparse vectors observed via few linear measurements can be reconstructed exactly. In the latter setting, the linear measurements are only available through their signs, so exact reconstruction of sparse vectors is repl...
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We give general identifiability conditions on the source matrix in Blind Signal Separation problem. They refine some previously known ones. We develop a subspace clustering algorithm, which is a generalization of the k-plane clustering algorithm, and is suitable for separation of sparse mixtures with bigger sparsity (i.e. when the number of the sensors is bigger at least by 2 than the number of...
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ژورنال
عنوان ژورنال: IEEE Transactions on Image Processing
سال: 2020
ISSN: 1057-7149,1941-0042
DOI: 10.1109/tip.2020.3027986