Alessandro Adamo

ECG compression retaining the best natural basis k-coefficients via sparse decomposition

Abstract A novel and efficient signal compression algorithm aimed at finding the sparsest representation of electrocardiogram (ECG) signals is presented and analyzed. The idea behind the method relies on basis elements drawn from the initial transitory of a signal itself, and the sparsity promotion process applied to its subsequent blocks grabbed by a sliding window. The saved coefficients rescaled in a convenient range, quantized and compressed by a lossless entropy-based algorithm. Experiments on signals extracted from the MIT-BIH Arrhythmia database show that the method achieves in most of the cases very high performance.

A fixed-point iterative schema for error minimization in k-sparse decomposition

Analogously to the well known greedy strategy called Orthogonal Matching Pursuit (OMP), we present a new algorithm to solve the sparse approximation problem over redundant dictionaries where the input signal is restricted to be a linear combination of k atoms or fewer from a fixed dictionary. The basic strategy of our method rests on a family of nonlinear mappings which results to be contractive in a interval close to zero. By iterating contractions and projections the method is able to extract the most significant components also for noisy signal which subsumes an ideal underlying signal having sufficiently sparse representation. For reasonable error level, the fixed point solution of such a iterative schema provides a sparse approximation containing only the nonzero terms characterizing the unique sparsest representation of the ideal noiseless sparse signal. The heuristic method so derived has been applied both to synthetic and real data. The former was generated by combining exact signals drawn by usual Bernoulli-Gaussian model and Gaussian noise; the later is taken by electrocardiogram (ECG) signals with application to the dictionary learning problem. In both cases the proposed method outperforms OMP method both regarding sparse approximation error and computation time.

Sparse representation based classification for face recognition by k -limaps algorithm

In this paper, we present a new approach for face recognition that is robust against both poorly defined and poorly aligned training and testing data even with few training samples. Working in the conventional feature space yielded by the Fishers Linear Discriminant analysis, it uses a recent algorithm for sparse representation, namely k-LiMapS, as general classification criterion. Such a technique performs a local l0 pseudo-norm minimization by iterating suitable parametric nonlinear mappings. Thanks to its particular search strategy, it is very fast and able to discriminate among separated classes lying in the low-dimension Fisherspace. Experiments are carried out on the FRGC version 2.0 database showing good classification capability even when compared with the state-of-the-art l1 norm-based sparse representation classifier (SRC).

Local features and sparse representation for face recognition with partial occlusions

In this paper we present a new local-based face recognition system that combines weak classifiers to create a robust system able to recognize faces in presence of either occlusions or large expression variations. The method relies on sparse approximation using dictionaries built on local features. Experiments on the AR database show the effectiveness of our method, which achieves better performance than those obtained by the state-of-the-art ℓ1 norm-based sparse representation classifier (SRC).

Robust face recognition using sparse representation in LDA space

In this article, we address the problem of face recognition under uncontrolled conditions. The proposed solution is a numerical robust algorithm dealing with face images automatically registered and projected via the linear discriminant analysis (LDA) into a holistic low-dimensional feature space. At the heart of this discriminative system, there are suitable nonconvex parametric mappings based on which a fixed-point technique finds the sparse representation of test images allowing their classification. We theoretically argue that the success achieved in sparsity promoting is due to the sequence of values imposed on a characteristic parameter of the used mapping family. Experiments carried out on several databases (ORL, YaleB, BANCA, FRGC v2.0) show the robustness and the ability of the system for classification purpose. In particular, within the area of sparsity promotion, our recognition system shows very good performance with respect to those achieved by the state-of-the-art

Face Recognition in Uncontrolled Conditions Using Sparse Representation and Local Features

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Trade-off between hops and delays in hub-based forwarding in DTNs

ℓ1 norm-based sparse representation classifier (SRC), the recently proposed