Bayesian Sparse Estimation for Background/Foreground Separation

2016 ◽  
pp. 481-498
Author(s):  
Shinichi Nakajima ◽  
Masashi Sugiyama ◽  
S Babacan
Keyword(s):  
Sparse Estimation

scholarly journals A Novel Detection Scheme with Multiple Observations for Sparse Signal Based on Likelihood Ratio Test with Sparse Estimation

2016 ◽  
Vol 2016 ◽  
pp. 1-14
Author(s):  
Hongbo Zhao ◽  
Lei Chen ◽  
Wenquan Feng ◽  
Chuan Lei
Keyword(s):  
Likelihood Ratio ◽  
Likelihood Ratio Test ◽  
Prior Information ◽  
Finite Size ◽  
Sparse Signals ◽  
Ratio Test ◽  
Sparse Estimation ◽  
Detection Scheme ◽  
Error Exponent ◽  
Multiple Observations

Recently, the problem of detecting unknown and arbitrary sparse signals has attracted much attention from researchers in various fields. However, there remains a peck of difficulties and challenges as the key information is only contained in a small fraction of the signal and due to the absence of prior information. In this paper, we consider a more general and practical scenario of multiple observations with no prior information except for the sparsity of the signal. A new detection scheme referred to as the likelihood ratio test with sparse estimation (LRT-SE) is presented. Under the Neyman-Pearson testing framework, LRT-SE estimates the unknown signal by employing thel1-minimization technique from compressive sensing theory. The detection performance of LRT-SE is preliminarily analyzed in terms of error probabilities in finite size and Chernoff consistency in high dimensional condition. The error exponent is introduced to describe the decay rate of the error probability as observations number grows. Finally, these properties of LRT-SE are demonstrated based on the experimental results of synthetic sparse signals and sparse signals from real satellite telemetry data. It could be concluded that the proposed detection scheme performs very close to the optimal detector.

Sparse estimation of backscattered echoes from underwater object using integrated dictionaries

2018 ◽  
Vol 144 (6) ◽  
pp. 3475-3484
Author(s):  
Xiangxia Meng ◽  
Xiukun Li ◽  
Andreas Jakobsson ◽  
Yahui Lei
Keyword(s):  
Sparse Estimation ◽  
Underwater Object

Sparse Estimation and Uncertainty with Application to Subgroup Analysis

2017 ◽  
Vol 25 (1) ◽  
pp. 1-40 ◽  
Author(s):  
Marc Ratkovic ◽  
Dustin Tingley
Keyword(s):  
Variable Selection ◽  
Confidence Intervals ◽  
Public Support ◽  
Theoretical Work ◽  
Bayesian Variable Selection ◽  
Order Effects ◽  
Selection Methods ◽  
Sparse Estimation ◽  
Recent Theoretical Work ◽  
Subgroup Effects

We introduce a Bayesian method, LASSOplus, that unifies recent contributions in the sparse modeling literatures, while substantially extending pre-existing estimators in terms of both performance and flexibility. Unlike existing Bayesian variable selection methods, LASSOplus both selects and estimates effects while returning estimated confidence intervals for discovered effects. Furthermore, we show how LASSOplus easily extends to modeling repeated observations and permits a simple Bonferroni correction to control coverage on confidence intervals among discovered effects. We situate LASSOplus in the literature on how to estimate subgroup effects, a topic that often leads to a proliferation of estimation parameters. We also offer a simple preprocessing step that draws on recent theoretical work to estimate higher-order effects that can be interpreted independently of their lower-order terms. A simulation study illustrates the method’s performance relative to several existing variable selection methods. In addition, we apply LASSOplus to an existing study on public support for climate treaties to illustrate the method’s ability to discover substantive and relevant effects. Software implementing the method is publicly available in theRpackagesparsereg.

Advanced Processing of a Blade Vibratory Response Obtained With Tip Timing Method Using Hyperparameter-Free Sparse Estimation Method

2021 ◽  
Author(s):  
Vsevolod Kharyton ◽  
Dave Zachariah
Keyword(s):  
Estimation Method ◽  
Field Measurements ◽  
Parameter Tuning ◽  
Test Cases ◽  
Time Data ◽  
Sparse Estimation ◽  
Vibratory Response ◽  
Blade Tip ◽  
Data Adaptive ◽  
Real Time Data Processing

Abstract The study presents the application of a sparse estimation method which enables explicit identification of spectrum components of a vibratory signal of a blade obtained by means of blade tip timing measurement. The method exploits the sparse frequency content of the blade vibratory response and uses a data-adaptive weighting to achieve sparsity. In contrast to other approaches, this method obviates the need for any parameter tuning during the identification process and admits an online formulation that renders it capable of real-time data processing. In the study only experimentally acquired data from either prototype testing or field measurements are used to evoke the method applicability. For some considered test cases there were no strain gauges available, therefore proposed method was the only means to study blades vibratory response.

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