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2012 IEEE International Workshop on Machine Learning for Signal Processing最新文献

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Distributed variational sparse Bayesian learning for sensor networks 传感器网络的分布式变分稀疏贝叶斯学习
2012 IEEE International Workshop on Machine Learning for Signal Processing Pub Date : 2012-11-12 DOI: 10.1109/MLSP.2012.6349800
Thomas Buchgraber, D. Shutin
{"title":"Distributed variational sparse Bayesian learning for sensor networks","authors":"Thomas Buchgraber, D. Shutin","doi":"10.1109/MLSP.2012.6349800","DOIUrl":"https://doi.org/10.1109/MLSP.2012.6349800","url":null,"abstract":"In this work we present a distributed sparse Bayesian learning (dSBL) regression algorithm. It can be used for collaborative sparse estimation of spatial functions in wireless sensor networks (WSNs). The sensor measurements are modeled as a weighted superposition of basis functions. When kernels are used, the algorithm forms a distributed version of the relevance vector machine. The proposed method is based on a combination of variational inference and loopy belief propagation, where data is only communicated between neighboring nodes without the need for a fusion center. We show that for tree structured networks, under certain parameterization, dSBL coincides with centralized sparse Bayesian learning (cSBL). For general loopy networks, dSBL and cSBL are differend, yet simulations show much faster convergence over the variational inference iterations at similar sparsity and mean squared error performance. Furthermore, compared to other sparse distributed regression methods, our method does not require any cross-tuning of sparsity parameters.","PeriodicalId":262601,"journal":{"name":"2012 IEEE International Workshop on Machine Learning for Signal Processing","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129577075","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: 7
Kernels for time series of exponential decay/growth processes 指数衰减/增长过程的时间序列的核
2012 IEEE International Workshop on Machine Learning for Signal Processing Pub Date : 2012-11-12 DOI: 10.1109/MLSP.2012.6349753
Zineb Noumir, P. Honeine, C. Richard
{"title":"Kernels for time series of exponential decay/growth processes","authors":"Zineb Noumir, P. Honeine, C. Richard","doi":"10.1109/MLSP.2012.6349753","DOIUrl":"https://doi.org/10.1109/MLSP.2012.6349753","url":null,"abstract":"Many processes exhibit exponential behavior. When kernel-based machines are applied on this type of data, conventional kernels such as the Gaussian kernel are not appropriate. In this paper, we derive kernels adapted to time series of exponential decay or growth processes. We provide a theoretical study of these kernels, including the issue of universality. Experimental results are given on a case study: chlorine decay in water distribution systems.","PeriodicalId":262601,"journal":{"name":"2012 IEEE International Workshop on Machine Learning for Signal Processing","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129820685","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
Opportunistic sensing: Unattended acoustic sensor selection using crowdsourcing models 机会感测:使用众包模型选择无人值守声学传感器
2012 IEEE International Workshop on Machine Learning for Signal Processing Pub Date : 2012-11-12 DOI: 10.1109/MLSP.2012.6349815
Po-Sen Huang, M. Hasegawa-Johnson, W. Yin, Thomas S. Huang
{"title":"Opportunistic sensing: Unattended acoustic sensor selection using crowdsourcing models","authors":"Po-Sen Huang, M. Hasegawa-Johnson, W. Yin, Thomas S. Huang","doi":"10.1109/MLSP.2012.6349815","DOIUrl":"https://doi.org/10.1109/MLSP.2012.6349815","url":null,"abstract":"Unattended wireless sensor networks have been widely used in many applications. This paper proposes automatic sensor selection methods based on crowdsourcing models in the Opportunistic Sensing framework, with applications to unattended acoustic sensor selection. Precisely, we propose two sensor selection criteria and solve them via greedy algorithm and quadratic assignment. Our proposed method achieves, on average, 5.64% higher accuracy than the traditional approach under sparse reliability conditions.","PeriodicalId":262601,"journal":{"name":"2012 IEEE International Workshop on Machine Learning for Signal Processing","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133129414","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
Mixture weight influence on kernel entropy component analysis and semi-supervised learning using the Lasso 混合权值对核熵成分分析和Lasso半监督学习的影响
2012 IEEE International Workshop on Machine Learning for Signal Processing Pub Date : 2012-11-12 DOI: 10.1109/MLSP.2012.6349814
Jonas Nordhaug Myhre, R. Jenssen
{"title":"Mixture weight influence on kernel entropy component analysis and semi-supervised learning using the Lasso","authors":"Jonas Nordhaug Myhre, R. Jenssen","doi":"10.1109/MLSP.2012.6349814","DOIUrl":"https://doi.org/10.1109/MLSP.2012.6349814","url":null,"abstract":"The aim of this paper is two-fold. First, we show that the newly developed spectral method known as kernel entropy component analysis (kernel ECA) captures cluster structure, which is very important in semi-supervised learning, and we provide an analysis showing how mixture weights influence kernel ECA in a mixture of cluster components setting. Second, we develop a semi-supervised kernel ECA classifier based on the Lasso framework, and report promising results compared to the state-of-the art.","PeriodicalId":262601,"journal":{"name":"2012 IEEE International Workshop on Machine Learning for Signal Processing","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116034337","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: 13
Microvascular blood flow estimation in sublingual microcirculation videos based on a principal curve tracing algorithm 基于主曲线跟踪算法的舌下微循环视频微血管血流估计
2012 IEEE International Workshop on Machine Learning for Signal Processing Pub Date : 2012-11-12 DOI: 10.1109/MLSP.2012.6349763
S. You, E. Cansizoglu, Deniz Erdoğmuş, Michael J. Massey, Nathan Shapiro
{"title":"Microvascular blood flow estimation in sublingual microcirculation videos based on a principal curve tracing algorithm","authors":"S. You, E. Cansizoglu, Deniz Erdoğmuş, Michael J. Massey, Nathan Shapiro","doi":"10.1109/MLSP.2012.6349763","DOIUrl":"https://doi.org/10.1109/MLSP.2012.6349763","url":null,"abstract":"Microcirculatory perfusion is an important metric for diagnosing pathological conditions in patients. Capillary density and red blood cell (RBC) velocity provide a measure of tissue perfusion. Estimating RBC velocity is a challenging problem due to noisy video sequences, low contrast between the vessels and the background, and thousands of RBCs moving rapidly through video sequences. Typically, physicians manually trace small blood vessels and visually estimate RBC velocities. The task is labor intensive, tedious, and time-consuming. In this paper, we present a novel application of a principal curve tracing algorithm to automatically track RBCs across video frames and estimate their velocity based on the displacements of RBCs between two consecutive frames. The proposed method is implemented in one sublingual microcirculation video of a healthy subject.","PeriodicalId":262601,"journal":{"name":"2012 IEEE International Workshop on Machine Learning for Signal Processing","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133310385","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: 3
Extraction of sparse spatial filters using Oscillating Search 基于振荡搜索的稀疏空间滤波器提取
2012 IEEE International Workshop on Machine Learning for Signal Processing Pub Date : 2012-11-12 DOI: 10.1109/MLSP.2012.6349752
I. Onaran, N. Ince, A. Abosch, A. Cetin
{"title":"Extraction of sparse spatial filters using Oscillating Search","authors":"I. Onaran, N. Ince, A. Abosch, A. Cetin","doi":"10.1109/MLSP.2012.6349752","DOIUrl":"https://doi.org/10.1109/MLSP.2012.6349752","url":null,"abstract":"Common Spatial Pattern algorithm (CSP) is widely used in Brain Machine Interface (BMI) technology to extract features from dense electrode recordings by using their weighted linear combination. However, the CSP algorithm, is sensitive to variations in channel placement and can easily overfit to the data when the number of training trials is insufficient. Construction of sparse spatial projections where a small subset of channels is used in feature extraction, can increase the stability and generalization capability of the CSP method. The existing ℓ0 norm based sub-optimal greedy channel reduction methods are either too complex such as Backward Elimination (BE) which provided best classification accuracies or have lower accuracy rates such as Recursive Weight Elimination (RWE) and Forward Selection (FS) with reduced complexity. In this paper, we apply the Oscillating Search (OS) method which fuses all these greedy search techniques to sparsify the CSP filters. We applied this new technique on EEG dataset IVa of BCI competition III. Our results indicate that the OS method provides the lowest classification error rates with low cardinality levels where the complexity of the OS is around 20 times lower than the BE.","PeriodicalId":262601,"journal":{"name":"2012 IEEE International Workshop on Machine Learning for Signal Processing","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133700606","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
A Kullback-Leibler divergence approach for wavelet-based blind image deconvolution 基于小波的盲图像反卷积的Kullback-Leibler散度方法
2012 IEEE International Workshop on Machine Learning for Signal Processing Pub Date : 2012-11-12 DOI: 10.1109/MLSP.2012.6349757
A. Seghouane, M. Hanif
{"title":"A Kullback-Leibler divergence approach for wavelet-based blind image deconvolution","authors":"A. Seghouane, M. Hanif","doi":"10.1109/MLSP.2012.6349757","DOIUrl":"https://doi.org/10.1109/MLSP.2012.6349757","url":null,"abstract":"A new algorithm for wavelet-based blind image restoration is presented in this paper. It is obtained by defining an intermediate variable to characterize the original image. Both the original image and the additive noise are modeled by multivariate Gaussian process. The blurring process is specified by its point spread function, which is unknown. The original image and the blur are estimated by alternating minimization of the KullbackLeibler divergence between a model family of probability distributions defined using a linear image model and a desired family of probability distributions constrained to be concentrated on the observed data. The intermediate variable is used to introduce regularization in the algorithm. The algorithm presents the advantage to provide closed form expressions for the parameters to be updated and to converge only after few iterations. A simulation example that illustrates the effectiveness of the proposed algorithm is presented.","PeriodicalId":262601,"journal":{"name":"2012 IEEE International Workshop on Machine Learning for Signal Processing","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133969908","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
Pattern search in dysfluent speech 语言障碍中的模式搜索
2012 IEEE International Workshop on Machine Learning for Signal Processing Pub Date : 2012-11-12 DOI: 10.1109/MLSP.2012.6349744
Juraj Pálfy, Jiri Pospíchal
{"title":"Pattern search in dysfluent speech","authors":"Juraj Pálfy, Jiri Pospíchal","doi":"10.1109/MLSP.2012.6349744","DOIUrl":"https://doi.org/10.1109/MLSP.2012.6349744","url":null,"abstract":"Pattern recognition in time series is often used in data mining and in bioinformatics. Speech can be considered only as a different type of signal and processed as a time series. Stuttered speech is rich in events also known as dysfluencies, typically repetitions. This paper describes a new method for enumerating complex repetitions. Classical approaches to stuttered speech analyzed dysfluencies in very short intervals, which were sufficient for recognizing simple repetitions of phonemes. However, the problem of repetitions of syllables or words was typically ignored due to high computational demands of classical methods for analysis of longer intervals. Our approach uses a method adopted from data mining and bioinformatics, together with efficient representation of speech signal, which simplifies processing of speech enough to enable analysis of longer intervals. Results show applicability of the proposed method.","PeriodicalId":262601,"journal":{"name":"2012 IEEE International Workshop on Machine Learning for Signal Processing","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133118695","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: 8
Efficient optimization for data visualization as an information retrieval task 数据可视化作为信息检索任务的高效优化
2012 IEEE International Workshop on Machine Learning for Signal Processing Pub Date : 2012-11-12 DOI: 10.1109/MLSP.2012.6349797
J. Peltonen, K. Georgatzis
{"title":"Efficient optimization for data visualization as an information retrieval task","authors":"J. Peltonen, K. Georgatzis","doi":"10.1109/MLSP.2012.6349797","DOIUrl":"https://doi.org/10.1109/MLSP.2012.6349797","url":null,"abstract":"Visualization of multivariate data sets is often done by mapping data onto a low-dimensional display with nonlinear dimensionality reduction (NLDR) methods. Many NLDR methods are designed for tasks like manifold learning rather than low-dimensional visualization, and can perform poorly in visualization. We have introduced a formalism where NLDR for visualization is treated as an information retrieval task, and a novel NLDR method called the Neighbor Retrieval Visualizer (NeRV) which outperforms previous methods. The remaining concern is that NeRV has quadratic computational complexity with respect to the number of data. We introduce an efficient learning algorithm for NeRV where relationships between data are approximated through mixture modeling, yielding efficient computation with near-linear computational complexity with respect to the number of data. The method inherits the information retrieval interpretation from the original NeRV, it is much faster to optimize as the number of data grows, and it maintains good visualization performance.","PeriodicalId":262601,"journal":{"name":"2012 IEEE International Workshop on Machine Learning for Signal Processing","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125032362","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: 3
Learning with the kernel signal to noise ratio 用核信噪比学习
2012 IEEE International Workshop on Machine Learning for Signal Processing Pub Date : 2012-11-12 DOI: 10.1109/MLSP.2012.6349715
L. Gómez-Chova, Gustau Camps-Valls
{"title":"Learning with the kernel signal to noise ratio","authors":"L. Gómez-Chova, Gustau Camps-Valls","doi":"10.1109/MLSP.2012.6349715","DOIUrl":"https://doi.org/10.1109/MLSP.2012.6349715","url":null,"abstract":"This paper presents the application of the kernel signal to noise ratio (KSNR) in the context of feature extraction to general machine learning and signal processing domains. The proposed approach maximizes the signal variance while minimizes the estimated noise variance in a reproducing kernel Hilbert space (RKHS). The KSNR can be used in any kernel method to deal with correlated (possibly non-Gaussian) noise. We illustrate the method in nonlinear regression examples, dependence estimation and causal inference, nonlinear channel equalization, and nonlinear feature extraction from high-dimensional satellite images. Results show that the proposed KSNR yields more fitted solutions and extracts more noise-free features when confronted with standard approaches.","PeriodicalId":262601,"journal":{"name":"2012 IEEE International Workshop on Machine Learning for Signal Processing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128790426","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: 6
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