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Jul 1, 2012 · We introduced two compressive sensing based dimensionality reduction algorithms. The core idea is to find a space, in which data are sparsely ...
This paper is concerned with the dimensionality reduction problem based on the compressive assumptions. We propose novel unsupervised and semi-supervised ...
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PDF | This paper presents a novel algorithm for the dimensionality reduction which employs compressed sensing (CS) to improve the generalization.
Feb 24, 2012 · Compressive sensing is an emerging field predicated upon the fact that, if a signal has a sparse represen- tation in some basis, ...
Abstract: This paper presents a novel algorithm for the dimensionality reduction which employs compressed sensing (CS) to improve the generalization ...
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Compressive sensing (CS) is a technique based on a proven observation that if a signal is compressible, i.e. has a sparse representation in some basis, it can ...
CS of- fers a framework for simultaneous sensing and compression of finite-dimensional vectors that re- lies on linear dimensionality reduction. Quite sur-.
Sep 11, 2016 · Such observations of dimensionality reduction allow compressive sensing to play a critical role in model building and assessment in the ...
Apr 5, 2012 · How much can a neural system reduce the dimensionality of its activity patterns without incurring a large loss in its ability to perform.
Compressed sensing and dimensionality reduction for unsupervised learning (2014) ; Contributions to signal analysis and processing using compressed sensing ...
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