检索结果-内蒙古大学图书馆

12th IEEE Asia-Pacific Conference on Antennas and Propagation, APCAP 2024

作者： Qin, Jiale Gui, Shuliang Tian, Zengshan Xiao, Ke Tian, Haitao Chongqing University of Posts and Telecommunications Chongqing400065 China Guangdong Institute of Electronic Information Engineering University of Electronic Science and Technology of China dongguan523000 China

ISBN: (纸本)9798350351019

In this paper, a two-dimensional off-grid direction of arrival (DOA) estimation strategy based on sparse Bayesian learning (SBL) is proposed for improving the two-dimensional DOA estimation accuracy of millimeter-wave multiple-input multiple-output (MIMO) radar in low signal-to-noise ratio (SNR) environment. First, the grid is employed alongside Taylor expansion to formulate the signal model. After that, the expectation maximization (EM) algorithm is employed in conjunction with the corresponding marginal probability density function to iteratively estimate the hyperparameters. Finally, the conditional probability density function of the received signal is utilized, from which the error weighting coefficients for off-grid DOA estimation are derived. The simulation combined with the data of Cascade MIMO radar demonstrates the effectiveness of the proposed method relative to other algorithms. © 2024 IEEE.

关键词： expectation maximization algorithm

来源：评论

学校读者我要写书评

暂无评论

Accurate Delayed Source Model for Multi-Frame Full-Rank Spatial Covariance Analysis 18

Accurate Delayed Source Model for Multi-Frame Full-Rank Spat...

引用

18th International Workshop on Acoustic Signal Enhancement, IWAENC 2024

作者： Furunaga, Shinya Sawada, Hiroshi Ikeshita, Rintaro Nakatani, Tomohiro Makino, Shoji Waseda University 2-7 Hibikino Wakamatsu-ku Kitakyushu Fukuoka808-0135 Japan Ntt Corporation 2-4 Hikaridai Seika-cho Soraku-gun Kyoto619-0237 Japan

ISBN: (纸本)9798350361858

Multi-frame Full-rank Spatial Covariance Analysis (mfFCA) is a technique for a blind source separation method and can be applied to reverberant underdetermined conditions where the sources outnumber the microphones and the reverberation time is long. This model, however, does not express all direct and delayed source components in multi-frame observation vector. This paper proposes a new model that takes into account accurately the direct and delayed source components, by introducing delay-wise spatial covariance matrices. We have then derived new expectation-maximization and multiplicative update algorithms for the proposed model. Experimental results show that the proposed method performed better than the conventional mfFCA for the task to separate three sources with two microphones. © 2024 IEEE.

关键词： expectation maximization algorithm

来源：评论

学校读者我要写书评

暂无评论

Accelerating the convergence of EM-based training algorithms for RBF networks

引用

6th International Work-Conference on Artificial and Natural Neural Networks, IWANN 2001

作者： Lázaro, Marcelino Santamaría, Ignacio Pantaleón, Carlos Dpto. Ing. Comunieaciones ETSII v Telecom Universidad de Cantabria Avda Los Castros 39005 Santander Spain

ISBN: (纸本)3540422358

In this paper, we propose a new expectation-maximization (EM) algorithm which speeds up the training of feedforward networks with local activation functions such as the Radial Basis Function (RBF) nctw ork. The core of the conditional EM algorithm for supervised learning of feedforward net w orks consists of decomposing the obserations into their individual output units and then estimating the parameters of each unit separately. In previously proposed approaches, at eac h *** the residual is decomposed equally among the units or proportionally to the weights of the output layer. However, this approach tends to slow down the training of networks with local activation units. To overcome this drawback in this paper we use a new E-step which applies a soft decomposition of the residual among the units. Inparticular, the residual is decomposed according to the probability of each RBF unit given each input-output pattern. It is shown that this variant not only speeds up the training in comparison with other EM-type1 algorithms, but also provides better results than a global gradient-descent, technique since it has the capability of avoiding some unw anted minima of the cost function. © Springer-Verlag Berlin Heidelberg 2001.

关键词： expectation maximization algorithm

来源：评论

学校读者我要写书评

暂无评论

Unsupervised ensemble learning for genome sequencing

引用

PATTERN RECOGNITION 2022年 129卷

作者： Pages-Zamora, Alba Ochoa, Idoia Cavero, Gonzalo Ruiz Villalvilla-Ornat, Pol Univ Politecn Catalunya BarcelonaTech UPC SPCOM Grp C-Jordi Girona 31 Barcelona 08034 Spain Univ Navarra Tecnun Manuel Lardizabal 13 San Sebastian 20018 Spain Swiss Fed Inst Technol Erdbeobachtung u Fernerkundung HCP G 33-1Leopold Ruzicka Weg 4 CH-8093 Zurich Switzerland

Unsupervised ensemble learning refers to methods devised for a particular task that combine data pro-vided by decision learners taking into account their reliability, which is usually inferred from the data. Here, the variant calling step of the next generation sequencing technologies is formulated as an unsuper-vised ensemble classification problem. A variant calling algorithm based on the expectation-maximization algorithm is further proposed that estimates the maximum-a-posteriori decision among a number of classes larger than the number of different labels provided by the learners. Experimental results with real human DNA sequencing data show that the proposed algorithm is competitive compared to state-of -the-art variant callers as GATK, HTSLIB, and Platypus.(c) 2022 The Author(s). Published by Elsevier *** is an open access article under the CC BY-NC-ND license ( http://***/licenses/by-nc-nd/4.0/ )

关键词： expectation maximization algorithm Variant calling Genome sequencing Unsupervised multi-class ensemble classifier GATK

来源：评论

学校读者我要写书评

暂无评论

A novel Mixture Model Method for identification of differentially expressed genes from DNA microarray data

引用

BMC BIOINFORMATICS 2004年第1期5卷 201-201页

作者： Najarian, K Zaheri, M Rad, AA Najarian, S Dargahi, J Univ N Carolina Dept Comp Sci Charlotte NC 28223 USA Amirkabir Univ Technol Comp Engn & IT Dept Tehran Iran Concordia Univ CONCAVE Res Ctr Dept Mech & Ind Engn Quebec City PQ Canada

Background: The main goal in analyzing microarray data is to determine the genes that are differentially expressed across two types of tissue samples or samples obtained under two experimental conditions. Mixture model method (MMM hereafter) is a nonparametric statistical method often used for microarray processing applications, but is known to over-fit the data if the number of replicates is small. In addition, the results of the MMM may not be repeatable when dealing with a small number of replicates. In this paper, we propose a new version of MMM to ensure the repeatability of the results in different runs, and reduce the sensitivity of the results on the parameters. Results: The proposed technique is applied to the two different data sets: Leukaemia data set and a data set that examines the effects of low phosphate diet on regular and Hyp mice. In each study, the proposed algorithm successfully selects genes closely related to the disease state that are verified by biological information. Conclusion: The results indicate 100% repeatability in all runs, and exhibit very little sensitivity on the choice of parameters. In addition, the evaluation of the applied method on the Leukaemia data set shows 12% improvement compared to the MMM in detecting the biologically-identified 50 expressed genes by Thomas et al. The results witness to the successful performance of the proposed algorithm in quantitative pathogenesis of diseases and comparative evaluation of treatment methods.

关键词： Bayesian Information Criterion expectation maximization Acute Myeloid Leukaemia Acute Lymphoblastic Leukaemia expectation maximization algorithm

来源：评论

学校读者我要写书评

暂无评论

Adaptive quantile low-rank matrix factorization

引用

PATTERN RECOGNITION 2020年 103卷 107310-107310页

作者： Xu, Shuang Zhang, Chun-Xia Zhang, Jiangshe Xi An Jiao Tong Univ Sch Math & Stat Xian 710049 Shaanxi Peoples R China

Low-rank matrix factorization (LRMF) has received much popularity owing to its successful applications in both computer vision and data mining. By assuming noise to come from a Gaussian, Laplace or mixture of Gaussian distributions, significant efforts have been made on optimizing the (weighted) L-1 or L-2-norm loss between an observed matrix and its bilinear factorization. However, the type of noise distribution is generally unknown in real applications and inappropriate assumptions will inevitably deteriorate the behavior of LRMF. On the other hand, real data are often corrupted by skew rather than symmetric noise. To tackle this problem, this paper presents a novel LRMF model called AQ-LRMF by modeling noise with a mixture of asymmetric Laplace distributions. An efficient algorithm based on the expectation-maximization (EM) algorithm is also offered to estimate the parameters involved in AQ-LRMF. The AQ-LRMF model possesses the advantage that it can approximate noise well no matter whether the real noise is symmetric or skew. The core idea of AQ-LRMF lies in solving a weighted L-1 problem with weights being learned from data. The experiments conducted on synthetic and real data sets show that AQ-LRMF outperforms several state-of-the-art techniques. Furthermore, AQ-LRMF also has the superiority over the other algorithms in terms of capturing local structural information contained in real images. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Low-rank matrix factorization Mixture of asymmetric Laplace distributions expectation maximization algorithm Skew noise

来源：评论

学校读者我要写书评

暂无评论

Effect of false positive and false negative rates on inference of binding target conservation across different conditions and species from ChIP-chip data

引用

BMC BIOINFORMATICS 2009年第1期10卷 23-23页

作者： Datta, Debayan Zhao, Hongyu Yale Univ Dept Biomed Engn New Haven CT 06520 USA Yale Univ Dept Epidemiol & Publ Hlth New Haven CT 06520 USA Yale Univ Dept Genet New Haven CT 06520 USA

Background: ChIP-chip data are routinely used to identify transcription factor binding targets. However, the presence of false positives and false negatives in ChIP-chip data complicates and hinders analyses, especially when the binding targets for a specific transcription factor are compared across conditions or species. Results: We propose an expectation maximization based approach to infer the underlying true counts of "positives" and "negatives" from the observed counts. Based on this approach, we study the effect of false positives and false negatives on inferences related to transcription regulation. Conclusion: Our results indicate that if there is a significant degree of association among the binding targets across conditions/species (log odds ratio > 4), moderate values of false positive and false negative rates (0.005 and 0.4 respectively) would not change our inference qualitatively (i.e. the presence or absence of conservation) based on the observed experimental data despite a significant change in the observed counts. However, if the underlying association is marginal, with odds ratios close to 1, moderate to large values of false positive and false negative rates (0.01 and 0.2 respectively) could mask the underlying association.

关键词： False Positive Rate Contingency Table False Negative Rate expectation maximization algorithm True Proportion

来源：评论

学校读者我要写书评

暂无评论

caBIG™ VISDA: Modeling, visualization, and discovery for cluster analysis of genomic data

引用

BMC BIOINFORMATICS 2008年第1期9卷 1-18页

作者： Zhu, Yitan Li, Huai Miller, David J. Wang, Zuyi Xuan, Jianhua Clarke, Robert Hoffman, Eric P. Wang, Yue Virginia Polytech Inst & State Univ Dept Elect & Comp Engn Arlington VA 22203 USA NIA Bioinformat Unit RRB NIH Baltimore MD 21224 USA Penn State Univ Dept Elect Engn University Pk PA 16802 USA Childrens Natl Med Ctr Med Genet Res Ctr Washington DC 20010 USA Georgetown Univ Dept Oncol Physiol & Biophys Washington DC 20007 USA Georgetown Univ Lombardi Comprehens Canc Ctr Washington DC 20007 USA

Background: The main limitations of most existing clustering methods used in genomic data analysis include heuristic or random algorithm initialization, the potential of finding poor local optima, the lack of cluster number detection, an inability to incorporate prior/expert knowledge, black-box and non-adaptive designs, in addition to the curse of dimensionality and the discernment of uninformative, uninteresting cluster structure associated with confounding variables. Results: In an effort to partially address these limitations, we develop the VIsual Statistical Data Analyzer (VISDA) for cluster modeling, visualization, and discovery in genomic data. VISDA performs progressive, coarse-to-fine ( divisive) hierarchical clustering and visualization, supported by hierarchical mixture modeling, supervised/unsupervised informative gene selection, supervised/unsupervised data visualization, and user/prior knowledge guidance, to discover hidden clusters within complex, high-dimensional genomic data. The hierarchical visualization and clustering scheme of VISDA uses multiple local visualization subspaces (one at each node of the hierarchy) and consequent subspace data modeling to reveal both global and local cluster structures in a "divide and conquer" scenario. Multiple projection methods, each sensitive to a distinct type of clustering tendency, are used for data visualization, which increases the likelihood that cluster structures of interest are revealed. Initialization of the full dimensional model is based on first learning models with user/prior knowledge guidance on data projected into the low-dimensional visualization spaces. Model order selection for the high dimensional data is accomplished by Bayesian theoretic criteria and user justification applied via the hierarchy of low-dimensional visualization subspaces. Based on its complementary building blocks and flexible functionality, VISDA is generally applicable for gene clustering, sample clustering, and phenoty

关键词： expectation maximization algorithm Minimum Description Length Locality Preserve Projection Emerin Discriminative Gene

来源：评论

学校读者我要写书评

暂无评论

A common random effect induced bivariate gamma degradation process with application to remaining useful life prediction

引用

RELIABILITY ENGINEERING & SYSTEM SAFETY 2022年第0期219卷 108200-108200页

作者： Song, Kai Cui, Lirong Beijing Inst Technol Sch Management & Econ Beijing Peoples R China Qingdao Univ Coll Qual & Standardizat Qingdao Peoples R China

Due to the complex structures and the multi-functionality of modern products, there are usually two or more performance characteristics which can reflect a product's degradation states. The degradation processes corresponding to these performance characteristics are dependent in general, which brings challenges to the degradation data analysis. In this paper, a gamma process based degradation model is developed for the bivariate dependent degradation data, where the dependency between the two degradation processes is captured by a common random effect naturally. The expectation maximization algorithm is employed to estimate the model parameters. Then, a real-time prediction method for a product's remaining useful life is proposed using the Bayesian method. Finally, both the simulation study and the case study are provided for illustration, whose results demonstrate that the proposed model as well as the corresponding inference methods does work well.

关键词： Bivariate degradation data expectation maximization algorithm Gamma process Maximum likelihood estimation Remaining useful life prediction

来源：评论

学校读者我要写书评

暂无评论

Point set registration via rigid transformation consensus

引用

COMPUTERS & ELECTRICAL ENGINEERING 2022年 101卷

作者： Li, Zhaolong Wang, Cheng Ma, Jieying Li, Zhongyu Zhu, Jihua Xi An Jiao Tong Univ Sch Software Engn Xi'an 710049 Peoples R China State Key Lab Rail Transit Engn Informatizat FSDI Xi'an 710043 Peoples R China

Point set registration is a fundamental problem in many domains. This paper proposes a novel pair-wise registration algorithm based on the rigid transformation consensus. It starts by building a point correspondence set, which contains both inliers and outliers. Due to non-overlapping regions, it associates each point correspondence with a latent variable and formulates pair-wise registration as a maximum likelihood estimation problem, which is optimized by the expectation-maximum algorithm. Since all inliers follows the consensus of one similar rigid transformation, each correspondence is assigned a posterior probability to indicate whether it is inlier or outlier. To obtain the desired result, it requires to alternatively implement the establishment of point correspondence and maximum likelihood estimation. Given initial rigid transformation, the proposed algorithm is able to obtain a desired registration result for the pair-wise registration. Experiments tested on public available data sets illustrate its superior performance on accuracy and efficiency over previous algorithms.

关键词： Point set registration expectation maximization algorithm Gaussian distribution Point correspondence Inlier Outlier

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：