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The conventional sparsity-based compressive sensing (CS) has been extended to a more general framework based on the broad class of manifold models. Although some existing manifold-based CS methods use a mixture of factor analyzers to discover the low-dimensional geometric structures of the signals, they have two issues that may limit their practical use: First, the signal representation using manifolds...
The broad class of manifold models are considered for extending the conventional compressive sensing (CS) to a more general framework. However, although the manifold-based CS approaches using a mixture of factor analyzers can learn latent geometric structures of high-dimensional signals, they have two issues that potentially limit their practical use: First, the manifold modeling does not take account...
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