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Many practical applications of clustering involve data collected over time. In these applications, evolutionary clustering can be applied to the data to track changes in clusters with time. In this paper, we consider an evolutionary version of spectral clustering that applies a forgetting factor to past affinities between data points and aggregates them with current affinities. We propose to use an...
This paper discusses the development of a recursive estimator which systematically arrives at sparse parameter estimates. Prior work achieved this by utilizing a Gaussian sum filter. This paper shows the relationship between the implementation using a Gaussian sum filter, where the mean and covariance of each component is propagated, and the equivalent representation using an information filter. We...
In a distributed estimation system, the fusion center receives the local estimates from sensors and fuses them to be an optimal estimation in terms of some criterion. Recently, the best linear unbiased estimation (BLUE) fusion was proposed to minimize the mean square error of the fused estimate, in which the weights to optimally combine the local estimates are determined by the covariance matrix of...
Double-difference carrier-phase relative positioning can realize GPS high-precision positioning. In the designing and implementation of post-processing technique, the key is resolving integer ambiguities. Applying with VC++ combining with MATCOM, the transformation from two dimension array to matrix has been realized, and matrix calculation is implemented in MATCOM, in which the LAMBDA algorithm has...
Software development cost overruns often induce project managers to cut down manpower cost at the expense of software quality. Accurate effort estimation is beneficial to the prevention of cost overruns. Analogy-based effort estimation predicts the effort of a new project by using the information of its similar historical projects, where the similarity is measured via Euclidean distance. To calculate...
A number of problems in computer vision require the estimation of a set of matrices, each of which is defined only up to an individual scale factor and represents the parameters of a separate model, under the assumption that the models are intrinsically interconnected. One example of such a set is a family of fundamental matrices sharing an infinite homography. Here an approach is presented to estimating...
In OFDMA multiple access wireless communication systems, carrier frequency offsets between the transmitter and the receiver tend to destroy the orthogonality among subcarriers, and hence, introduces intercarrier interference. A two-stage frequency offset estimation algorithm based on subspace processing is proposed. The main advantage of the proposed method is that it can obtain the CFOs of all users...
Let the observed sequence {yk} be generated by the multivariate ARMAX system A(z)yk = B(z)uk-1 + C(z)wk, where {wk} is the system noise with unknown covariance matrix Rw > 0, and {uk} is a sequence of mutually independent and identically distributed (iid) random vectors. Based on {yk} and {uk}, identification algorithms are proposed to simultaneously estimate the orders (p,q,r), the covariance...
An algorithm of adaptive deformation estimation of moving object in mean-shift tracking method is developed. Firstly, the differences between object and background in the weight image associated with the target candidate region are analyzed. By their different distributions, the area estimation of the target is converted into the task of image segmentation. The threshold is automatically selected...
In this article, we consider the estimation for the vector of parameters in a linear regression model by unifying the sample and the prior information. A new Liu-type biased estimator called weighted stochastic restricted Liu estimator is proposed. Furthermore, necessary and sufficient conditions for the superiority of the weighted stochastic restricted Liu estimator over the Liu estimator, the weighted...
Group Lasso is an efficient regularized least-square regression algorithm, and is now being used as a computationally feasible method to select grouped variables. In this paper, we address the issue of estimation consistency of the group Lasso with special diagonal matrix. We derive sufficient condition for the consistency of group Lasso under practical assumptions, such as model misspecification...
The problem of robust system identification with corrupted data remains a difficulty. In this paper we shall put ourselves in the prediction error framework. We shall present a mixed L1-L2 estimator based on a parameterized objective function leading to an alternative solution fighting against the outliers, based on the well-known Huber's M-estimate. A simple physical insight on the main noise characteristics...
Although there are some recent characterizations of Multivariate Gauss Markov-Random Field (MGMRF) models, these are limited to cases where the interaction matrix coefficients are modeled with some special form. We extend the modeling and parameter estimation for the interaction matrix coefficients for a general anisotropic MGMRF. Although the MGMRF is a natural generalization of its univariate counterpart,...
We consider the problem of estimating the central direction of arrival (DOA) of multiple coherently distributed sources. This problem is encountered due to the presence of local scatters in the vicinity of a transmitter or due to signals propagating through a random inhomogeneous medium. Since the integral steering vector of coherently distributed source can be deduced to be a Schur-Hadamard product...
The statistical description of an optimal sampling reconstruction procedure (SRP) of non stationary Gaussian fields is given on the basis of the conditional mean rule. The non stationarity of field is determined in space, along of one axis, but not in time. A new type of the spatial covariance function of non stationary Gaussian field is suggested. A non trivial example of SRP of non stationary Gaussian...
In this paper, we derive a maximum a posteriori (MAP) classifier using the features extracted by biased discriminant analysis (BDA) in multi-class classification problems. Using the one-against-the-rest scheme we construct several feature spaces, where the MAP classifier is formulated. Although the maximum likelihood (ML) classifier is generally equivalent to the MAP classifier when the prior probability...
In the paper, we consider the computation of the posterior Cramer-Rao bound in a problem of target tracking based on received signal strength measurements. This is a theoretical lower bound on the estimation error while assessing the performance of any kind of estimation algorithm. Here the method is applied to a nonlinear filtering problem of tracking a node in wireless sensor networks. Specifically...
We consider the problem of estimating the parameters-the central direction of arrival (DOA) and angular spread of a coherently distributed source. This problem is encountered due to the presence of local scatterers in the vicinity of a transmitter or due to signals propagating through a random inhomogeneous medium. Since the computational complexity of the parameter estimation is normally highly demanding,...
3D point data acquired from laser scan or stereo vision can be quite noisy. A preprocessing step is often needed before a surface reconstruction algorithm can be applied. In this paper, we propose a nonparametric approach for noisy point data preprocessing. In particular, we proposed an anisotropic kernel based nonparametric density estimation method for outlier removal, and a hill-climbing line search...
This paper gives the models of uncertain multisensor system based on norm-bounded parameter uncertain model method and convex bounded uncertain model method, respectively. The corresponding centralized robust fusion estimations are developed, too. Simulation compares different fusion estimation methods detailedly in frequency and time fields and concludes that each of two filters has its strong point...
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