Global stabilization of a class of partially known nonnegative systems

In this paper we deal with the problem of global output feedback stabilization of a class of n-dimensional nonlinear nonnegative systems possessing a one-dimensional analytically unknown part that is also a measured output. We first propose our main result, an output feedback control procedure, taking advantage of measurements of the uncertain part, able to globally stabilize the system toward an adjustable equilibrium point in the positive orthant. Though quite general, this result is based on hypotheses that might be difficult to check in practice. Then in a second step, through a theorem on a class of nonnegative systems linking the existence of a positive equilibrium to its global asymptotic stability, we propose other hypotheses for our main result to hold. These new hypotheses are more restrictive but much simpler to check. An illustrative example highlights both the potentially complex open loop dynamics of the considered systems and the interesting characteristics of the control procedure.     

一种非负矩阵分解的快速方法

针对超高维数据进行非负矩阵分解的计算代价大,特征提取速度慢问题,提出一种非负矩阵分解的快速算法。该算法通过代数变换,把对原高维矩阵的非负分解转换成非负的低维矩阵的非负分解,其求解过程只需要对一个阶数等于样本数的对角矩阵进行非负矩阵分解,同时提取某样本特征时只需要计算该样本与所有训练样本的内积。对高维小样本的基因表达数据降维后进行k均值聚类分析,实验结果表明,该算法在不影响非负矩阵分解性能的前提下,大大提高了计算速度。     

非负张量分解的快速算法*

作为非负矩阵分解的多线性推广,非负张量分解已被成功地应用在信号处理、计算机视觉、数据挖掘和神经科学等领域中.提出了非负张量分解的一种快速算法.首先,将大的张量数据视做多元连续函数的离散化,对其进行采样得到一个小张量;其次,对小张量执行非负分解,可得到它的重构张量;然后,对于采样后的重构张量,使用二维线性插值方法对原始张量进行重构;最后,实验结果表明快速张量分解算法的有效性.     

Distributed constraint satisfaction with partially known constraints

Distributed constraint satisfaction problems (DisCSPs) are composed of agents connected by constraints. The standard model for DisCSP search algorithms uses messages containing assignments of agents. It assumes that constraints are checked by one of the two agents involved in a binary constraint, hence the constraint is fully known to both agents. This paper presents a new DisCSP model in which constraints are kept private and are only partially known to agents. In addition, value assignments can also be kept private to agents and not be circulated in messages. Two versions of a new asynchronous backtracking algorithm that work with partially known constraints (PKC) are presented. One is a two-phase asynchronous backtracking algorithm and the other uses only a single phase. Another new algorithm preserves the privacy of assignments by performing distributed forward-checking (DisFC). We propose to use entropy as quantitative measure for privacy. An extensive experimental evaluation demonstrates a trade-off between preserving privacy and the efficiency of search, among the different algorithms. Partially supported by the Spanish project TIN2006-15387-C03-01. Partially supported by the Lynn and William Frankel center for Computer Sciences and the Paul Ivanier Center for Robotics and Production Management.     

虚拟人引导的脑电信号重要性选择与识别

为了在缺失数据和噪声数据的脑电信号中保持较好的鲁棒性,并揭示脑电信号多通道之间相互作用关系,利用随机森林算法挑选出具有相互作用的重要通道,去除不相关和冗余的通道;利用状态空间模型描述多通道之间的内部运动规律,反映输入输出与内部状态之间的关系;采用EM算法实现状态空间模型的参数辨识作为识别特征;将提取的特征通过SE-GRU模型进行识别,增加了重要特征的权重.上述方法在公共数据集和虚拟人引导的脑电信号数据集上有效提高了分类准确率,相比不进行通道选择的方法取得了更好的效果,并通过最终训练模型实现了对虚拟人的控制.     

Using the EM algorithm for inference in a mixture of distributions with censored but partially identifiable data

This paper describes an implementation of the EM algorithm for the statistical analysis of a finite mixture of distributions arising when data are censored but partially identifiable. We consider a scheme of type I censoring where censoring times are random. The estimation of standard errors proposed by Meng and Rubin (1991. Using EM to obtain asymptotic variance-covariance matrices: the SEM algorithm. J. Amer. Statist. Assoc. 86(416), 899-909) is also implemented in the context of the above mixture. A Bayesian method introduced in Contreras-Cristán et al. (2003. Statistical inference for mixtures of distributions for censored data with partial identification. Commun. in Statist. Theory Methods 32(4), 749-774) for the case of a constant censoring value is extended to the case of random censoring times. Comparisons with different methods are carried out both with simulated data and with the observations on failure times for communication transmitter-receivers of Mendenhall and Hader (1958. Estimation of parameters of mixed exponentially distributed failure time distributions from censored life test data. Biometrika 45, 504-520).     

Model-based transductive learning of the kernel matrix

This paper addresses the problem of transductive learning of the kernel matrix from a probabilistic perspective. We define the kernel matrix as a Wishart process prior and construct a hierarchical generative model for kernel matrix learning. Specifically, we consider the target kernel matrix as a random matrix following the Wishart distribution with a positive definite parameter matrix and a degree of freedom. This parameter matrix, in turn, has the inverted Wishart distribution (with a positive definite hyperparameter matrix) as its conjugate prior and the degree of freedom is equal to the dimensionality of the feature space induced by the target kernel. Resorting to a missing data problem, we devise an expectation-maximization (EM) algorithm to infer the missing data, parameter matrix and feature dimensionality in a maximum a posteriori (MAP) manner. Using different settings for the target kernel and hyperparameter matrices, our model can be applied to different types of learning problems. In particular, we consider its application in a semi-supervised learning setting and present two classification methods. Classification experiments are reported on some benchmark data sets with encouraging results. In addition, we also devise the EM algorithm for kernel matrix completion. Editor: Philip M. Long     

Cooperative adaptive sampling of random fields with partially known covariance

This paper considers autonomous robotic sensor networks taking measurements of a physical process for predictive purposes. The physical process is modeled as a spatiotemporal random field. The network objective is to take samples at locations that maximize the information content of the data. The combination of information‐based optimization and distributed control presents difficult technical challenges as standard measures of information are not distributed in nature. Moreover, the lack of prior knowledge on the statistical structure of the field can make the problem arbitrarily difficult. Assuming the mean of the field is an unknown linear combination of known functions and its covariance structure is determined by a function known up to an unknown parameter, we provide a novel distributed method for performing sequential optimal design by a network comprised static and mobile devices. We characterize the correctness of the proposed algorithm and examine in detail the time, communication, and space complexities required for its implementation. Copyright © 2011 John Wiley & Sons, Ltd.     

图像分割的自适应交互核图割模型

为了克服图割模型算法在实现图像分割时需要人为选定参数,以及图割模型可能会陷入局部最小值的不足,考虑到交互图割是一种灵活的全局最优算法,提出了基于EM方法的交互核图割算法。数据映射到核空间,构造了新的目标函数,这样可以更有效地解决分类分割问题;为了估计交互图割所需要的参数以及图割算法所需要的各种阈值,采用EM算法来估计这些参数,避免人为随机选取可能造成的不利影响,因而该方法是一种自适应的分割算法。实验结果表明,相对于交互图割算法,该算法分割合成图像时具有更低的误分率,处理光学等图像时,分割结果更准确,保留图像细节信息的能力更强。     

基于多段分频等长信号融合的频率估计方法

多段采样信号十分常见,对其进行信息融合能有效提高信号处理的精度,尤其适用于低信噪比、被测频率持续时间短的情况。为提高多段采样信号频率估计的精度和扩展已有方法的适用范围,给出一种多段分频等长信号融合方法。在该方法中,因各段信号的被测频率不等,故生成频域分析参数矩阵以实现同频化效果;因同频化后各段信号之间仍然相位不连续,故设计相位差补偿因子矩阵以达到相位连续信号的效果;因相位差补偿因子矩阵包含未知参数,故生成搜索频率序列以用于实际计算并得到具有特定形式的功率谱矩阵。为验证方法的正确性,给出了数学证明。针对多种应用环境状态进行了仿真实验,结果表明该方法具有普适性,抗噪性好,频率估计精度比现有方法有较大提高。     

高斯扩散特性图象的盲解卷积

图象的盲解卷积恢复具有重要的理论和实际意义,许多情况下系统的扩散特性不能精确获得。针对一类相对平滑或类似高斯分布的扩散特性,建立一种图象盲解卷积算法,采用交替迭代方法。适合总体最小二乘求解。算法能有效地确定点扩散函数,图象恢复质量有明显改善。最后的仿真实验表明了算法的有效性和稳定性。     

双核压缩激活神经网络艺术图像分类

目的 为了充分提取版画、中国画、油画、水彩画和水粉画等艺术图像的整体风格和局部细节特征,实现计算机自动分类检索艺术图像的需求,提出通过双核压缩激活模块（double kernel squeeze-and-excitation,DKSE）和深度可分离卷积搭建卷积神经网络对艺术图像进行分类。方法 根据SKNet（selective kernel networks）自适应调节感受野提取图像整体与细节特征的结构特点和SENet（squeeze-and-excitation networks）增强通道特征的特点构建DKSE模块,利用DKSE模块分支上的卷积核提取输入图像的整体特征与局部细节特征;将分支上的特征图进行特征融合,并对融合后的特征图进行特征压缩和激活处理;将处理后的特征加权映射到不同分支的特征图上并进行特征融合;通过DKSE模块与深度可分离卷积搭建卷积神经网络对艺术图像进行分类。结果 使用本文网络模型对有无数据增强（5类艺术图像数据增强后共25 634幅）处理的数据分类,数据增强后的分类准确率比未增强处理的准确率高9.21%。将本文方法与其他网络模型和传统分类方法相比,本文方法的分类准确率达到86.55%,比传统分类方法高26.35%。当DKSE模块分支上的卷积核为1×1和5×5,且放在本文网络模型第3个深度可分离卷积后,分类准确率达到87.58%。结论 DKSE模块可以有效提高模型分类性能,充分提取艺术图像的整体与局部细节特征,比传统网络模型具有更好的分类准确率。     

贝叶斯网络参数的在线学习算法及应用

以EM算法为基础，在给定贝叶斯网络结构情况下。研究分析了Voting EM算法并利用该算法对防洪决策贝叶斯网络进行在线参数学习，将该算法与EM算法的学习结果进行了比较分析，结果表明Voting EM算法不但能够进行在线参数学习，而且也具有较高的学习精度．     

含随机测量时延的EWMA批间控制器设计

批间控制(RtR)是半导体晶圆生产过程控制的有效算法. 然而, 受测量手段与测量成本的限制, 难以实时检 测晶圆的品质数据, 即: 存在一定的测量时延, 通常该测量时延是随机, 时变的, 且直接影响批间控制器的性能. 为 此, 本文基于指数加权移动平均(EWMA)算法, 提出一种含随机测量时延的扰动估计方法. 在分析测量概率的基础 上, 建立包含测量时延概率的扰动估计表达式; 并采用期望最大化(EM)算法估计该测量时延的概率; 然后分析系统 可能存在的静差项, 给出相应的补偿算法; 最后讨论系统的稳定性. 仿真实例验证所提算法的有效性.     

基于EM-SMC的STBC-MC-CDMA信号盲检测

针对多径衰落条件下的空时分组编码多载波码分多址(STBC-MC-CDMA)信号检测问题,在分析信号模型的基础上,提出了一种期望值最大化(EM)框架下基于序贯蒙特卡洛(SMC)的盲检测算法。首先对接收的信号按用户数进行分解,然后估计各分信号的未知参量,并将结果反馈,进行循环运算,最后得到各用户的信息序列。仿真结果表明,与其他算法相比,性能更加优异,当用户数不超过10、扩频码长度G>16、信噪比大于6 dB时,恢复出的信息序列误码率低于10-2。     

多输出数据依赖核支持向量回归快速学习算法

针对基于递推下降法的多输出支持向量回归算法在模型参数拟合过程中收敛速度慢、预测精度低的情况,使用一种基于秩2校正规则且具有二阶收敛速度的修正拟牛顿算法（BFGS）进行多输出支持向量回归算法的模型参数拟合,同时为了保证模型迭代过程中的下降量和全局收敛性,应用非精确线性搜索技术确定步长因子。通过分析支持向量机（SVM）中核函数的几何结构,构造数据依赖核函数替代传统核函数,生成多输出数据依赖核支持向量回归模型。将模型与基于梯度下降法、修正牛顿法拟合的多输出支持向量回归模型进行对比。实验结果表明,在200个样本下该算法的迭代时间为72.98 s,修正牛顿法的迭代时间为116.34 s,递推下降法的迭代时间为2065.22 s。所提算法能够减少模型迭代时间,具有更快的收敛速度。     

Learning from partially supervised data using mixture models and belief functions

This paper addresses classification problems in which the class membership of training data are only partially known. Each learning sample is assumed to consist of a feature vector x_i∈X and an imprecise and/or uncertain “soft” label m_i defined as a Dempster-Shafer basic belief assignment over the set of classes. This framework thus generalizes many kinds of learning problems including supervised, unsupervised and semi-supervised learning. Here, it is assumed that the feature vectors are generated from a mixture model. Using the generalized Bayesian theorem, an extension of Bayes’ theorem in the belief function framework, we derive a criterion generalizing the likelihood function. A variant of the expectation maximization (EM) algorithm, dedicated to the optimization of this criterion is proposed, allowing us to compute estimates of model parameters. Experimental results demonstrate the ability of this approach to exploit partial information about class labels.     

EM算法与K-Means算法比较

聚类是广泛应用的基本数据挖掘方法之一,它按照数据的相似性和差异性将数据分为若干簇,并使得同簇的尽量相似,不同簇的尽量相异.目前存在大量的聚类算法,本文仅考察了划分方法中的两个常用算法:EM算法和K-Means算法,并重点剖析了EM算法,对实验结果进行了分析.最后对算法进行了总结与讨论.     

一种关于蛋白质模体的自动识别算法

随着生物信息学的发展，模体识别已经成为一种能够从生物序列中提取有用生物信息的方法。文中介绍了有关模体的一些概念，讨论了模体识别算法(MEME)的基础，即EM(expectation maximization)算法，由于MEME算法是建立在EM算法的基础上的，所以又由此引出了MEME算法，并对MEME算法的一些基本问题比如时间复杂度、算法性能等进行了详细讨论，对算法的局限性和有待改进的地方作了说明。实践证明，MEME是一个较好的模体识别算法，它能够识别出蛋白质或者DNA序列中单个或多个模体，具有很大的灵活性。     

Solving weak transduction with EM

In this paper we describe an algorithm designed for learning perceptual organization of an autonomous agent. The learning algorithm performs incremental clustering of a perceptual input under reward. The distribution of the input samples is modeled by a Gaussian mixture density, which serves as a state space for the policy learning algorithm. The agent learns to select actions in response to the presented stimuli simultaneously with estimating the parameters of the input mixture density. The feedback from the environment is given to the agent in the form of a scalar value, or a reward, which represents the utility of a particular clustering configuration for the action selection. The setting of the learning task makes it impossible to use supervised or partially supervised techniques to estimate the parameters of the input density. The paper introduces the notion of weak transduction and shows a solution to it using an EM-based framework.