sparse approximation
Iterative method for simultaneous sparse approximation
... Abstract. This paper studies the problem of Simultaneous Sparse Approximation (SSA). This problem arises in many applications that work with multiple signals maintaining some degree of dependency, e.g., ...
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Approximate Submodularity and its Applications: Subset Selection, Sparse Approximation and Dictionary Selection
... Subset Selection for Regression. To illustrate the usefulness of the approximate sub- modularity framework, we analyze greedy algorithms for the problem of Subset Selection for Regression: select a subset of k variables ...
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Structure based learning in wireless networks via sparse approximation
... graph. Sparse approximation (and in particular the least-squares CS (LS CS) algorithm [15]) is then employed to identify a concise set of substructures to represent the cost function of ...
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Wavelet decompositions of Random Forests - smoothness analysis, sparse approximation and applications
... a sparse approximation of any regres- sion or classification high dimensional function at various levels of detail, with a concrete ordering of the Random Forest nodes: from ‘significant’ elements to nodes ...
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Denoising by Sparse Approximation: Error Bounds Based on Rate-Distortion Theory
... a sparse representation with respect to a frame, it can be estimated from a noise-corrupted observation y by finding the best sparse approximation to ...
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Learning Incoherent Dictionaries for Sparse Approximation Using Iterative Projections and Rotations
... best approximation quality, while a Gabor initialisation results in a lower coherence at the expense of a worse SNR ...the sparse approximation for every learned dictionary and exhibits a clear ...
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Sparse Approximation of Images Inspired from the Functional Architecture of the Primary Visual Areas
... for sparse ap- proximation of natural images based both on classical image processing criteria and on the known physiology of the pri- mary visual cortex (V1) of ...a sparse approximation procedure ...
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Compressed Sensing, Sparse Approximation, and Low-Rank Matrix Estimation
... Beginning with this line of work, much of the theory of CS concentrated on RIP conditions. We pause to note that although the RIP was introduced to the CS community by Cand` es–Tao [42], similar constructions had already ...
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Multistep Sparse Approximation Technology in Information Retrieval
... matrix approximation to approximate the matrix of ...matrix approximation. In this approach, a series of simple sparse matrices are used to approximate the de- ...
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Approximation Hardness for A Class of Sparse Optimization Problems
... We are interested in the computational complexity of Problems 1, 2 and 3 under general conditions of the loss function ` and the sparse penalty p. In particular, we focus on the case where ` is a convex loss ...
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More Constraints, Smaller Coresets: Constrained Matrix Approximation of Sparse Big Data
... Unlike previous results that apply only for the Frobenius norm (z = 2), and non-negativity constraints, our coreset and LTAS can be computed for a more general family of constraints and any z ≥ 1 to obtain a ...
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A sparse Markov chain approximation of LQ-type stochastic control problems
... an approximation of certain optimal control problems for reversible diffusions by Markov decision ...A sparse approximation that uses the basis of committor functions of metastable sets of the ...
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Sparse deterministic approximation of Bayesian inverse problems
... whether sparse approximation techniques can be used to approximate the posterior density and conditional expectations given the ...study sparse representations of the posterior ...
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Sparse Grid Interpolation
... the sparse grids is to break the curse of ...the sparse grids of combination technique and proved that the hierarchical sparse grid interpolation is equivalent to the interpolant using combination ...
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Adaptively Sparse Transformers
... Interrogation head. On the other hand, in Fig- ure 9 we show a head for which our adaptively sparse model chose an α close to 1, making it closer to softmax (also shown in encoder, layer 1, head 3 depicted in ...
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Recovering sparse graphs
... of sparse graphs, in particular to graphs locally excluding a minor by Dawar, Grohe and Kreutzer [5] and to the very general graph classes with bounded expansion, which were introduced in [18–21], by Dawar and ...
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From Sparse Solutions of Systems of Equations to Sparse Modeling of Signals and Images
... The PCA technique models each patch as a realization of a multivariate Gaussian distribution, and the learned dictionary is simply the set of usual principal axes based on an empirical covariance matrix. The K-SVD ...
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Degree of approximation theorems for approximation with side conditions
... of uniform approximation of a function, f, on an interval, by polynomials of degree not exceeding V satisfying a fixed set of Hermite-Birkhoff interpolatory side conditions of order K...[r] ...
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Sparse coding
... a sparse code and a target output can be associated by a linear function, then it will also be possible to linearly associate any invertible linear transform of the code with the same target ...
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Sparse Single-Index Model
... The use of MCMC methods has become a popular way to compute Bayesian estimates. For an introduction to the domain, one should refer to the comprehensive monograph of Marin and Robert (2007) and the references therein. ...
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