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This paper mainly studies the relief extraction and data processing in image fusion design. To depend on plane interception and geometric detail extraction in detail shift of point model, intercepting plane is built and the segmentation between relief and base plane of relief product is realized through plane interception. Then, the extracted intermediate relief is performed the isolated sampling...
The task of determining informative sensors and clustering the sensor measurements according to their information content is considered. To this end, the standard canonical correlation analysis (CCA) framework is equipped with norm-one and norm-two regularization terms to estimate the unknown number of field sources and identify informative groups of sensors. Coordinate descent techniques are combined...
In this paper, we present a novel framework, called learning by propagability, for two essential data mining tasks, i.e., classification and regression. The whole learning process is driven by the philosophy that the data labels and the optimal feature representation jointly constitute a harmonic system, where the data labels are invariant with respect to the propagation on the similarity graph constructed...
In this article we present a new approach to extract points that belongs to several ellipses or circles presented on a same image and with the presence of outliers. Each geometric form is extracted by means of a robust fitting, that is a nonlinear optimization problem, solved with two different heuristics: differential evolution and RANSAC. Once the geometric form is fitted, its points are extracted...
Very Fast Decision Tree (VFDT) in data stream mining has been widely studied for more than a decade. VFDT in essence can mine over a portion of an unbounded data stream at a time, and the structure of the decision tree gets updated whenever new data feed in; hence it can predict better upon the input of fresh data. Inherent from traditional decision trees that use information gains for tree induction,...
Today's applications deal with multiple types of information: graph data to represent the relations between objects and attribute data to characterize single objects. Analyzing both data sources simultaneously can increase the quality of mining methods. Recently, combined clustering approaches were introduced, which detect densely connected node sets within one large graph that also show high similarity...
Most well-known discriminative clustering models, such as spectral clustering (SC) and maximum margin clustering (MMC), are non-Bayesian. Moreover, they merely considered to embed domain-dependent prior knowledge into data-specific kernels, while other forms of prior knowledge were seldom considered in these models. In this paper, we propose a Bayesian maximum margin clustering model (BMMC) based...
The DNA microarray technology enables rapid, large scale screening for patterns of gene expression. It is meaningful to detect useful phenotypes and the informative genes that can manifest these phenotypes in gene expression data. While the existing methods of phenotypes discriminating are most supervised methods, they train samples based on the known informative genes. In this paper, we propose an...
The performance optimization of many man-made systems belong to simulation-based constrained optimization (SBCO), where the evaluation of both the performance and the constraint have no closed form expression and are based on simulation. The simulation-based estimate of both the performance and the feasibility are usually time-consuming and noisy. So it is of great practical interest to study how...
Along with the burst of open source projects, software theft (or plagiarism) has become a very serious threat to the healthiness of software industry. Software birthmark, which represents the unique characteristic of a program, can be used for software theft detection. We propose two system call based software birthmarks: SCSSB (system call short sequence birthmark) and IDSCSB (input dependant system...
In this article a primal barrier interior-point method for moving horizon estimation (MHE) is presented. It exploits the structure of the KKT systems yielding an algorithm with linear complexity in the horizon length as opposed to cubically as in unstructured solvers. Ideas of square root covariance Kalman filtering are proposed in order to update covariance matrices occurring in the factorization...
This paper addresses the problem of robust identification of a class of discrete-time affine hybrid systems, switched affine models, in a set membership framework. Given a finite collection of noisy input/output data and a bound on the number of subsystems, the objective is to identify a suitable set of affine models along with a switching sequence that can explain the available experimental information...
This paper is concerned with the problem of receding horizon control of discrete-time systems subject to possibly unbounded random noise inputs, while satisfying hard bounds on the control inputs. We use a nonlinear feedback policy with respect to noise measurements and show that the resulting mathematical program has a tractable convex solution. Moreover, under the assumption that the zero-input...
Invasive weed optimization (IWO) has been found to be a simple but powerful algorithm for function optimization over continuous spaces. It has reportedly outperformed many types of evolutionary algorithms and other search heuristics when tested over both benchmark and real-world problems. However the performance of most search heuristics deteriorates severely when applied to the task of optimization...
The study of multi-objective optimization has matured to a level where uncertainty is considered when comparing and evaluating solutions for any given problem. This paper reviews the current techniques that have been proposed to include uncertainty within a multi-objective framework. Probabilistic as well as fuzzy methods are reviewed. A new method to identify sample representative solutions from...
This paper discusses the robust fault detection design for the criteria such as ??-/????,??2/???? and????/???? in which Gd, the transfer function from disturbance to the measurement output, is a tall transfer matrix and Dd is not full column rank. It is shown that the faults in a subspace can be made arbitrarily sensitive, while the faults in the complementary subspace have bounded sensitivities that...
This paper describes and analyzes population size management, which can be used to enhance the efficiency of the extended compact genetic algorithm (ECGA). The ECGA is a selectorecombinative algorithm that requires an adequate sampling to generate a high-quality model of the problem. Population size management decreases the overall running time of the optimization process by splitting the algorithm...
This paper presents methodological advances on pulse measurement through thermal imaging of the face - a modality that recovers thermo-physiological function. Two previous methods that capitalized on heat transfer effects along and across the vessel during pulse propagation have been brought together in a fusion scheme. In addition, the quality of the extracted physiological signals has improved thanks...
To extend GA's application, that is important to study on genetic algorithm under noise environment. This paper firstly described the noise environment of the GA, analyzed the effect on GA of noise; then two indexes were proposed to evaluate the performance of GA in the noisy environment, CBMPGA was proposed for the noisy optimization, the numerical experiment shows that the performance of CBMPGA...
Based on the definition of arbitrage portfolio and its return introduced in Fang (2006), the mean-VaR analysis for arbitrage portfolios is presented. The calculation of the mean-VaR arbitrage frontier is discussed which is related to the mean-variance arbitrage frontier. Moreover a practical example is presented.
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