Gradient Estimation with Simultaneous Perturbation and Compressive Sensing
This paper aims at achieving a "good" estimator for the gradient of a function on a high-dimensional space. Often such functions are not sensitive in all coordinates and the gradient of the function is almost sparse. We propose a method for gradient estimation that combines ideas from Spall's Simultaneous Perturbation Stochastic Approximation with compressive sensing. The aim is to obtain "good" estimator without too many function evaluations. Application to estimating gradient outer product matrix as well as standard optimization problems are illustrated via simulations.
Code (0)
등록된 구현이 없습니다.
Tasks
Compressive SensingSimilar Papers 제목 키워드 기반
Compressive Recovery of Signals Defined on Perturbed Graphs
Recovery of signals with elements defined on the nodes of a graph, from compressive measurements is an important problem, which can arise in various domains such as sensor networks, image reconstruction and group testing…
compressed sensingImage ReconstructionModel SelectionStructural Group Sparse Representation for Image Compressive Sensing Recovery
Compressive Sensing (CS) theory shows that a signal can be decoded from many fewer measurements than suggested by the Nyquist sampling theory, when the signal is sparse in some domain. Most of conventional CS recovery ap…
Compressive SensingCompressive Sensing Based Adaptive Defence Against Adversarial Images
Herein, security of deep neural network against adversarial attack is considered. Existing compressive sensing based defence schemes assume that adversarial perturbations are usually on high frequency components, whereas…
Adversarial AttackCompressive SensingCovariance Estimation from Compressive Data Partitions using a Projected Gradient-based Algorithm
Compressive covariance estimation has arisen as a class of techniques whose aim is to obtain second-order statistics of stochastic processes from compressive measurements. Recently, these methods have been used in variou…
Compressive SensingEvaluation of the Effects of Compressive Spectrum Sensing Parameters on Primary User Behavior Estimation
As the Internet of Things (IoT) technology is being deployed, the demand for radio spectrum is increasing. Cognitive radio (CR) is one of the most promising solutions to allow opportunistic spectrum access for IoT second…
Compressive Sensing