A Greedy Sparse Method Suitable for Spectral-Line Estimation

Rapport interne de GIPSA-labThis letter presents a variant of Matching Pursuit (MP) method for compressive sensing and sparse signal reconstruction. As an extension of MP, the proposed algorithm incorporates a new backward technique to maintain or replace the previous selected atoms in the case of coherent dictionaries. Computer simulations using Fourier dictionaries are conducted to show the effectiveness of the proposed method compared to some other sparse approximation methods

Analysis of the blast-induced vibration structure in open-cast mines

Blasting in opencast mines is characterized by the use of large masses of explosives for a single blast. Blasting is done in a series of several to tens or even hundreds of charges placed in long holes and fired with a millisecond delay. Works are often carried out in the vicinity of buildings; therefore, reducing vibration impact is essential for opencast mines. This paper presents the applicability of the method of time-frequency Matching Pursuit (MP) for analysis of vibration structure. The use of MP analysis enables the development of much deeper and more reliable impact assessments of blasting on the environment

Single channel speech-music separation using matching pursuit and spectral masks

A single-channel speech music separation algorithm based on matching pursuit (MP) with multiple dictionaries and spectral masks is proposed in this work. A training data for speech and music signals is used to build two sets of magnitude spectral vectors of each source signal. These vectors’ sets are called dictionaries, and the vectors are called atoms. Matching pursuit is used to sparsely decompose the magnitude spectrum of the observed mixed signal as a nonnegative weighted linear combination of the best atoms in the two dictionaries that match the mixed signal structure. The weighted sum of the resulting decomposition terms that include atoms from the speech dictionary is used as an initial estimate of the speech signal contribution in the mixed signal, and the weighted sum of the remaining terms for the music signal contribution. The initial estimate of each source is used to build a spectral mask that is used to reconstruct the source signals. Experimental results show that integrating MP with spectral mask gives good separation results

Greedy Algorithms for Cone Constrained Optimization with Convergence Guarantees

Greedy optimization methods such as Matching Pursuit (MP) and Frank-Wolfe (FW) algorithms regained popularity in recent years due to their simplicity, effectiveness and theoretical guarantees. MP and FW address optimization over the linear span and the convex hull of a set of atoms, respectively. In this paper, we consider the intermediate case of optimization over the convex cone, parametrized as the conic hull of a generic atom set, leading to the first principled definitions of non-negative MP algorithms for which we give explicit convergence rates and demonstrate excellent empirical performance. In particular, we derive sublinear ($\mathcal{O}(1/t)$) convergence on general smooth and convex objectives, and linear convergence ($\mathcal{O}(e^{-t})$) on strongly convex objectives, in both cases for general sets of atoms. Furthermore, we establish a clear correspondence of our algorithms to known algorithms from the MP and FW literature. Our novel algorithms and analyses target general atom sets and general objective functions, and hence are directly applicable to a large variety of learning settings.Comment: NIPS 201

Batch-iFDD for representation expansion in large MDPs

Matching pursuit (MP) methods are a promising class of feature construction algorithms for value function approximation. Yet existing MP methods require creating a pool of potential features, mandating expert knowledge or enumeration of a large feature pool, both of which hinder scalability. This paper introduces batch incremental feature dependency discovery (Batch-iFDD) as an MP method that inherits a provable convergence property. Additionally, Batch-iFDD does not require a large pool of features, leading to lower computational complexity. Empirical policy evaluation results across three domains with up to one million states highlight the scalability of Batch-iFDD over the previous state of the art MP algorithm.United States. Office of Naval Research (Grant N00014-07-1-0749)United States. Office of Naval Research (Grant N00014-11-1-0688

Noise and vibration assessment of permanent-magnet synchronous motors based on matching pursuit

This paper presents a noise and vibration assessment scheme for the elevator permanent-magnet synchronous motors (PMSMs) based on matching pursuit (MP) with carefully selected atoms. The atomic dictionary is developed by considering of the complication of electromagnetic noise and vibration of the elevator PMSMs. After identifying the natural frequencies by modal testing and computing the characteristic electromagnetic frequencies of the PMSMs, the impulse energy ratio based on transient components, which are extracted by projecting on the selected atoms based on the MP method, are computed and used to assess the machines. The assessing results indicate that the transient components can accurately represent the electromagnetic impulse since the distorted magnetic fields and the features are robust for quality inspection of the elevator PMSMs

MATCHING PURSUIT ALGORITHM IN ASSESSING THE STATE OF ROLLING BEARINGS

In this paper the results of Matching Pursuit (MP) Octave algorithm applied to noise, vibration and harness (NVH) diagnosis of rolling bearings are presented. For this purpose two bearings in different condition state were examined. The object of the analysis was to calculate and present which energy error values of MP algorithm give the most accuracy results for different changes in bearing structures and also how energy values spread in time-frequency domain for chosen energy error value