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This study addresses the modal retrieval problem.
Then the R-D modal retrieval problem can be formulated as a penalized ℓ2 - ℓ0 sparse signal estimation problem (2).
In Section 4, we see how the 1-D modal retrieval problem may be addressed using sparse approximations and how the multigrid approach can be applied.
Similar to the 1-D case, the R-D modal retrieval problem can be formulated as a sparse signal estimation problem by defining a dictionary that gathers all possible combinations of 1-D modes obtained by sampling damping factors and frequencies for each dimension on 2-D grids.
(ii) We show how the 1-D modal retrieval problem can be addressed using sparse estimation approach by building a dictionary whose atoms are calculated by sampling the modal function over a 2-D grid (frequency and damping factor) in order to obtain all possible modes combinations.
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A special case of modal estimation is the harmonic retrieval problem (null damping factor) which has been formulated as a sparse approximation in a number of contributions.
AR: Twitter is a giant info retrieval problem.
These retrieval methods are based on extracting features which are tuned for the particular retrieval problem.
New metrics, visualizations and optimization tools are needed to address this "early retrieval" problem [38 40].
Figure 17 The results of recovery with GS-PhSparse and GS for large-scale retrieval problem.
In the fourth example, a three-dimensional harmonic retrieval problem of size 7×7×7 is simulated.
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