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2018 IEEE Statistical Signal Processing Workshop (SSP)最新文献

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Random Matrix-Optimized High-Dimensional MVDR Beamforming 随机矩阵优化的高维MVDR波束形成
2018 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2018-06-01 DOI: 10.1109/SSP.2018.8450743
Liusha Yang, M. Mckay, Romain Couillet
{"title":"Random Matrix-Optimized High-Dimensional MVDR Beamforming","authors":"Liusha Yang, M. Mckay, Romain Couillet","doi":"10.1109/SSP.2018.8450743","DOIUrl":"https://doi.org/10.1109/SSP.2018.8450743","url":null,"abstract":"A new approach to minimum variance distortionless response (MVDR) beamforming is proposed under the assumption of simultaneously large numbers of array sensors and observations. The key to our method is the design of an inverse covariance estimator which is appropriately optimized for the MVDR application. This is obtained by exploiting spectral properties of spiked covariance models in random matrix theory. Our proposed solution is simple to implement and is shown to yield performance improvements over competing approaches.","PeriodicalId":330528,"journal":{"name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122111441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
SSP 2018 TOC
2018 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2018-06-01 DOI: 10.1109/ssp.2018.8450693
{"title":"SSP 2018 TOC","authors":"","doi":"10.1109/ssp.2018.8450693","DOIUrl":"https://doi.org/10.1109/ssp.2018.8450693","url":null,"abstract":"","PeriodicalId":330528,"journal":{"name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123273379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A Two-Stage Approach to Robust Tensor Decomposition 鲁棒张量分解的两阶段方法
2018 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2018-06-01 DOI: 10.1109/SSP.2018.8450832
Seyyid Emre Sofuoglu, Selin Aviyente
{"title":"A Two-Stage Approach to Robust Tensor Decomposition","authors":"Seyyid Emre Sofuoglu, Selin Aviyente","doi":"10.1109/SSP.2018.8450832","DOIUrl":"https://doi.org/10.1109/SSP.2018.8450832","url":null,"abstract":"The rapid advance in sensor technology and computing systems has lead to the increase in the availability of multidimensional (tensor) data. Tensor data analysis have witnessed increasing applications in machine learning, data mining and computer vision. Traditional tensor decomposition methods such as Tucker decomposition and PARAFAC/CP decomposition aim to factorize the input tensor into a number of low-rank factors. However, they are prone to gross error that may occur due to illumination, occlusion or salt and pepper noise encountered in practical applications. For this purpose, higher order robust PCA (HoRPCA) and other robust tensor decomposition (RTD) methods have been proposed. These methods still have some limitations including sensitivity to non-sparse noise and high computational complexity. In this paper, we introduce a two-stage approach that combines HoRPCA with Higher Order SVD (HoSVD) to address these challenges.","PeriodicalId":330528,"journal":{"name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116598277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 5
Quantifying Hardware Related Attenuation from the Analysis of Nearby Microwave Links 从邻近微波链路分析量化硬件相关衰减
2018 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2018-06-01 DOI: 10.1109/SSP.2018.8450825
M. Fencl, V. Bareš
{"title":"Quantifying Hardware Related Attenuation from the Analysis of Nearby Microwave Links","authors":"M. Fencl, V. Bareš","doi":"10.1109/SSP.2018.8450825","DOIUrl":"https://doi.org/10.1109/SSP.2018.8450825","url":null,"abstract":"Commercial microwave links (CMLs), widely used as a backhaul of cellular networks, can be used for rainfall retrieval. Major uncertainties in CML rainfall estimation arise from hardware related attenuation, which is difficult to quantify. The contribution suggests using close-by CMLs to quantify this attenuation. The approach is tested on two years of data from three CMLs operating at 38 GHz. The results show that hardware related attenuation can reach more than 10 dB and that these extreme values are associated with heavy rainfalls. However, high attenuation values can occur also during light rainfalls and dew events. We suggest, that hardware related attenuation follows different functional relation during light and during heavy rainfalls. During light rainfalls (and dew events), attenuation gradually increases, however, during heavy rainfalls it is rather dependent on rainfall intensity.","PeriodicalId":330528,"journal":{"name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120956924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Wavelet Domain Bootstrap for Testing the Equality of Bivariate Self-Similarity Exponents 检验二元自相似指数的小波域自举法
2018 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2018-06-01 DOI: 10.1109/SSP.2018.8450710
H. Wendt, P. Abry, G. Didier
{"title":"Wavelet Domain Bootstrap for Testing the Equality of Bivariate Self-Similarity Exponents","authors":"H. Wendt, P. Abry, G. Didier","doi":"10.1109/SSP.2018.8450710","DOIUrl":"https://doi.org/10.1109/SSP.2018.8450710","url":null,"abstract":"Self-similarity has been widely used to model scale-free dynamics, with significant successes in numerous applications that are very different in nature. However, such successes have mostly remained confined to univariate data analysis while many applications in the modern “data deluge” era involve multivariate and dependent data. Operator fractional Brownian motion is a multivariate self-similar model that accounts for multivariate scale-free dynamics and characterizes data by means of a vector of self-similarity exponents (eigenvalues). This naturally raises the challenging question of testing the equality of exponents. Expanding on the recently proposed wavelet eigenvalue regression estimator of the vector of self-similarity exponents, in the present work we construct and study a wavelet domain bootstrap test for the equality of self-similarity exponents from one single observation (time series) of multivariate data. Its performance is assessed in a bivariate setting for various choices of sample size and model parameters, and it is shown to be satisfactory for use on real world data. Practical routines implementing estimation and testing are available upon request.","PeriodicalId":330528,"journal":{"name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129893681","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 4
A Convex Low-Rank Regularization Method for Hyperspectral Super-Resolution 高光谱超分辨率的凸低秩正则化方法
2018 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2018-06-01 DOI: 10.1109/SSP.2018.8450712
Ruiyuan Wu, Qiang Li, Xiao Fu, Wing-Kin Ma
{"title":"A Convex Low-Rank Regularization Method for Hyperspectral Super-Resolution","authors":"Ruiyuan Wu, Qiang Li, Xiao Fu, Wing-Kin Ma","doi":"10.1109/SSP.2018.8450712","DOIUrl":"https://doi.org/10.1109/SSP.2018.8450712","url":null,"abstract":"Hyperspectral super-resolution (HSR) is a technique of recovering a super-resolution image from a hyperspectral image (which has low spatial but high spectral resolutions) and a multispectral image (which has high spatial but low spectral resolutions). The problem is an ill-posed inverse problem in general, and thus judiciously designed formulations and algorithms are needed for good HSR performance. In this work, we employ the idea of low rank modeling, which was proven effective in helping enhance performance of HSR. Unlike the extensively employed nonconvex structured matrix factorization-based methods, we propose to use a convex regularizer for promoting low rank. Both unconstrained and constrained formulations are considered: the unconstrained case is tackled by the proximal gradient (PG) algorithm; while the more physically sound but challenging constrained case is solved by a custom-designed PG like algorithm, which uses the ideas of smoothing and majorization-minimization. Simulations are employed to showcase the effectiveness of the proposed methods.","PeriodicalId":330528,"journal":{"name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133178254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Compressive Online Decomposition of Dynamic Signals Via N-ℓ1 Minimization With Clustered Priors 基于聚类先验N- 1最小化的动态信号在线压缩分解
2018 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2018-06-01 DOI: 10.1109/SSP.2018.8450742
Huynh Van Luong, N. Deligiannis, Søren Forchhammer, André Kaup
{"title":"Compressive Online Decomposition of Dynamic Signals Via N-ℓ1 Minimization With Clustered Priors","authors":"Huynh Van Luong, N. Deligiannis, Søren Forchhammer, André Kaup","doi":"10.1109/SSP.2018.8450742","DOIUrl":"https://doi.org/10.1109/SSP.2018.8450742","url":null,"abstract":"We introduce a compressive online decomposition via solving an ${n}$-$ell _{1}$ cluster-weighted minimization to decompose a sequence of data vectors into sparse and low-rank components. In contrast to conventional batch Robust Principal Component Analysis (RPCA)—which needs to access full data—our method processes a data vector of the sequence per time instance from a small number of measurements. The $n-ell _{1}$ cluster-weighted minimization promotes (i) the structure of the sparse components and (ii) their correlation with multiple previously-recovered sparse vectors via clustering and re-weighting iteratively. We establish guarantees on the number of measurements required for successful compressive decomposition under the assumption of slowly-varying low-rank components. Experimental results show that our guarantees are sharp and the proposed algorithm outperforms the state of the art.","PeriodicalId":330528,"journal":{"name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129447752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Learning Dags using Multiclass Support Vector Machines 使用多类支持向量机学习包
2018 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2018-06-01 DOI: 10.1109/SSP.2018.8450754
F. Nikolay, M. Pesavento
{"title":"Learning Dags using Multiclass Support Vector Machines","authors":"F. Nikolay, M. Pesavento","doi":"10.1109/SSP.2018.8450754","DOIUrl":"https://doi.org/10.1109/SSP.2018.8450754","url":null,"abstract":"In this paper we consider the problem of learning the geneticinteraction- network that is underlying the measured double knockout (DK) data. Based on the biological system model of [3], we propose a multiclass-SVM approach that yields a high prediction accuracy of the genetic-interaction-network underlying the DK data while being able to estimate the network topology for large sets of genes. We demonstrate the performance of our proposed multiclass-SVM approach by synthetic data simulations where we use the recently proposed GENIE method of [3] as a benchmark.","PeriodicalId":330528,"journal":{"name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114557894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Non-Parametric Bayesian Inference for Change Point Detection in Neural Spike Trains 神经脉冲序列变化点检测的非参数贝叶斯推理
2018 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2018-06-01 DOI: 10.1109/SSP.2018.8450787
Bastian Alt, Michael Messer, J. Roeper, Gaby Schneider, H. Koeppl
{"title":"Non-Parametric Bayesian Inference for Change Point Detection in Neural Spike Trains","authors":"Bastian Alt, Michael Messer, J. Roeper, Gaby Schneider, H. Koeppl","doi":"10.1109/SSP.2018.8450787","DOIUrl":"https://doi.org/10.1109/SSP.2018.8450787","url":null,"abstract":"We present a model for point processes with gamma distributed increments. We assume a piecewise constant latent process controlling shape and scale of the distribution. For the discrete number of states of the latent process we use a non-parametric assumption by utilizing a Chinese restaurant process (CRP). For the inference of such inhomogeneous gamma processes with an unbounded number of states we do Bayesian inference using Markov Chain Monte Carlo. Finally, we apply the inference algorithm to simulated point processes and to empirical spike train recordings, which inherently possess non-stationary and non-Poissonian behavior.","PeriodicalId":330528,"journal":{"name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114558168","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Dempster-Shafer Theory Based Robust Sequential Detection in Distributed Sensor Networks 基于Dempster-Shafer理论的分布式传感器网络鲁棒序列检测
2018 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2018-06-01 DOI: 10.1109/SSP.2018.8450760
Mark R. Leonard, Christian A. Schroth, A. Zoubir
{"title":"Dempster-Shafer Theory Based Robust Sequential Detection in Distributed Sensor Networks","authors":"Mark R. Leonard, Christian A. Schroth, A. Zoubir","doi":"10.1109/SSP.2018.8450760","DOIUrl":"https://doi.org/10.1109/SSP.2018.8450760","url":null,"abstract":"We propose a distributed sequential detector based on the Dempster-Shafer Theory of Evidence. First, we introduce a novel rule for the basic probability assignment. This rule is based on the distribution of the likelihood ratio and is shown to yield better results than existing ones while at the same time avoiding counter-intuitive and contradictory probability assignments. Second, we use the Dempster-Shafer combination rule to design a distributed sequential detection algorithm. Third, we show how to robustify the algorithm against outliers by leveraging neighborhood communication.","PeriodicalId":330528,"journal":{"name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132829264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
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