A simple test to check the optimality of sparse signal approximations
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The aim of this work package (WP) is to explore approaches to learn structured sparse models, that is sparse models where the sparsity assumption seems not to be sufficient, or when there is hope to exploit some additional knowledge together with the spars ...
This paper presents a new method for learning overcomplete dictionaries adapted to efficient joint representation of stereo images. We first formulate a sparse stereo image model where the multi-view correlation is described by local geometric transforms o ...
Institute of Electrical and Electronics Engineers2011
We introduce the Multiplicative Update Selector and Estimator (MUSE) algorithm for sparse approximation in under-determined linear regression problems. Given ƒ = Φα* + μ, the MUSE provably and efficiently finds a k-sparse vector α̂ such that ∥Φα̂ − ƒ∥∞ ≤ ∥ ...
In this work we present a new greedy algorithm for sparse approximation called LocOMP. LocOMP is meant to be run on local dictionaries made of atoms with much shorter supports than the signal length. This notably encompasses shift-invariant dictionaries an ...
We propose and analyze acceleration schemes for hard thresholding methods with applications to sparse approximation in linear inverse systems. Our acceleration schemes fuse combinatorial, sparse projection algorithms with convex optimization algebra to pro ...
Spie-Int Soc Optical Engineering, Po Box 10, Bellingham, Wa 98227-0010 Usa2011
We provide two compressive sensing (CS) recovery algorithms based on iterative hard-thresholding. The algorithms, collectively dubbed as algebraic pursuits (ALPS), exploit the restricted isometry properties of the CS measurement matrix within the algebra o ...
Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2011
This paper shows the construction of an algorithm to calculate the roots of both, the cross-product of Bessel functions respectively of their first derivatives. The algorithm has shown globally convergent behavior for a large range of values of the argumen ...
We propose a new method for imaging sound speed in breast tissue from measurements obtained by ultrasound tomography (UST) scan- ners. Given the measurements, our algorithm finds a sparse image representation in an overcomplete dictionary that is adapted t ...
We develop an efficient learning framework to construct signal dictionaries for sparse representation by selecting the dictionary columns from multiple candidate bases. By sparse, we mean that only a few dictionary elements, compared to the ambient signal ...
Sparse methods are widely used in image and audio processing for denoising and classification, but there have been few previous applications to neural signals for brain-computer interfaces (BCIs). We used the dictionary- learning algorithm K-SVD, coupled w ...
