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
Fast and robust Earth Mover's Distances
Ofir Pele,Michael Werman +1 more
TL;DR: A new algorithm is presented for a robust family of Earth Mover's Distances - EMDs with thresholded ground distances so that the number of edges is reduced by an order of magnitude, which makes it possible to compute the EMD on large histograms and databases.
Journal ArticleDOI
Sensitivity to Basis Mismatch in Compressed Sensing
TL;DR: This paper establishes achievable bounds for the l1 error of the best k -term approximation and derives bounds, with similar growth behavior, for the basis pursuit l1 recovery error, indicating that the sparse recovery may suffer large errors in the presence of basis mismatch.
Posted Content
Compressed Sensing off the Grid
TL;DR: In this article, the frequency components of a mixture of s complex sinusoids from a random subset of n regularly spaced samples are estimated using an atomic norm minimization approach to exactly recover the unobserved samples.
Journal ArticleDOI
Compressed Sensing and Redundant Dictionaries
TL;DR: In this article, the concept of compressed sensing was extended to signals that are not sparse in an orthonormal basis but rather in a redundant dictionary, and it was shown that a matrix, which is a composition of a random matrix of certain type and a deterministic dictionary, has small restricted isometry constants.
MonographDOI
The random projection method
TL;DR: This paper presents a meta-modelling framework for embedding metrics in Euclidean space using a random projection approach and shows how this approach can be improved on the basis of prior work on similar models.