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This sparse representation problem appears in many applications.
The document representation problem: An Analysis of LSI and Iterative Residual Rescaling.
The original sparse representation problem is regularized with a properly designed weighted ℓ1 minimization and effectively solved with existing solver.
He then addresses three major problems with the first: (i) the majority rule problem; (ii) the representation problem; and (iii) the adversarial format problem.
The basic idea is to cast recognition as a sparse representation problem, utilizing new mathematical tools from compressed sensing and L1 minimization.
By assuming the intractability of the Discrete Logarithm Representation Problem and the q-Strong Diffie–Hellman Problem, our new scheme is proven secure under the random oracle model.
This paper made major research on the target representation problem, which plays a significant role in visual tracking, but has received little attention in most researches.
We carry out a comprehensive survey of available strategies for seeking global minima by comparing their performances in the binary representation problem.
Different from traditional manifold learning methods, which construct an adjacency graph from K-nearest neighbors or ɛ-ball method, the SRPE algorithm constructs the adjacency graph by solving a robust sparse representation problem through convex optimization.
Finally, the surrogate representation problem is connected to multi-parametric optimization and bounds on surrogate compositions are calculated as a function of the experimental uncertainty along with comparisons against experimental results.
In the scheme, the analysis sparse representation problem can be cast into row-to-row optimizations with respect to the analysis dictionary, and then the quadratic programming (QP) technique is used to optimize each row.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com