Compressive parameter estimation via K-median clustering
Dian Mo,Marco F. Duarte +1 more
TLDR
The use of earth mover’s distance (EMD), as applied to a pair of true and estimated PD coefficient vectors, to measure the parameter estimation error and it is shown that the EMD provides a better-suited metric for parameter estimation performance than the Euclidean distance.About:
This article is published in Signal Processing.The article was published on 2018-01-01 and is currently open access. It has received 6 citations till now. The article focuses on the topics: Estimation theory & Sparse approximation.read more
Citations
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Journal ArticleDOI
Overlap Aware Compressed Signal Classification
TL;DR: This paper proposes the use of a machine learning method known as overlap aware learning along with CSP that generates a smoother decision boundary and hence improves the classification accuracy at higher undersampling factors and simulation results show the trend of improved classification accuracy using the proposed method.
Proceedings ArticleDOI
A Comprehensive Review of Machine Learning in Multi-objective Optimization
TL;DR: In this article, the authors provide a global view of ML methods for multi-objective optimization problems and a reference for applying ML methods to solve a specific type of MOPs.
Dissertation
Compressive Acquisition and Processing of Sparse Analog Signals
TL;DR: The ways the application of a compressive measurement kernel impacts the signal recovery performance are looked into and methods to infer the current signal complexity from the compressive observations are investigated.
Journal ArticleDOI
An improved (1+1) evolutionary algorithm for k-median clustering problem with performance guarantee
TL;DR: It is proved that the (1+1) EA can obtain a performance guarantee of 5 for k -median problem in polynomial expected runtime O ( m n 2 ⋅ d m a x ) if all distances between data points and cluster centers are integers.
Journal ArticleDOI
Partially Coupled Stochastic Gradient Estimation for Multivariate Equation-Error Systems
TL;DR: By expanding the scalar innovation of each subsystem model to the innovation vector, a partially coupled multi-innovation generalized stochastic gradient algorithm is proposed and indicates that the proposed algorithms are effective and have good parameter estimation performances.
References
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Proceedings ArticleDOI
TLS-ESPRIT in a time delay estimation
TL;DR: In this article, the TLS-ESPRIT is applied to estimate the unknown frequencies of sinusoids in a white nonstationary noise, and it is shown that if the noise (after the deconvolution) is a white stationary process, then TLS-EPRIT derived returns to TLS-EsPRIT for the stationary noise case, and the computer simulations show that the derived method produces unbiased, high resolution time delay estimates.
Journal ArticleDOI
A Sparsity-Based Method for the Estimation of Spectral Lines From Irregularly Sampled Data
TL;DR: This paper deals with the l1-norm penalization of complex-valued variables, that brings satisfactory prior modeling for the estimation of spectral lines and proposes an efficient optimization strategy.
Journal ArticleDOI
Why Gabor frames? Two fundamental measures of coherence and their role in model selection
TL;DR: In this article, a model-order agnostic one-step thresholding (OST) algorithm was proposed for model selection with random or deterministic design matrices and arbitrary nonzero entries of the signal.
Journal ArticleDOI
Multiobjective Optimization of OFDM Radar Waveform for Target Detection
TL;DR: A constrained MOO-based algorithm is proposed to optimally design the spectral parameters of the OFDM waveform for the next coherent processing interval by simultaneously optimizing two objective functions: minimizing the upper bound on the estimation error to improve the efficiency of sparse-recovery and maximizing the squared Mahalanobis-distance to increase the performance of the underlying detection problem.
Journal ArticleDOI
Why Gabor Frames? Two Fundamental Measures of Coherence and Their Role in Model Selection
TL;DR: This paper studies non-asymptotic model selection for the general case of arbitrary (random or deterministic) design matrices and arbitrary nonzero entries of the signal and proves that OST is feasible for exact as well as partial model selection as long as the design matrix obeys an easily verifiable property, which is termed as the coherence property.