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Multiple view data with different feature representations have widely arisen in various practical applications. Due to the information diversity, fusing multiview features is very valuable for classification purpose. In this paper, we propose a new multifeature fusion method called fractional-order discriminative multiview correlation projection (FDMCP), which is based on fractional-order scatter...
Feature fusion plays an important role in target recognition, especially when single sensor's recognition capability is limited under severe situations. In view of shortcomings of Multi-set Canonical Correlation Analysis (MCCA) and its supervised modified methods in using category information in fusion projection rule learning, a generalized discriminative learning version of MCCA, termed as GDMCCA,...
This paper addresses the problem of joint detection and estimation fusion when sensor quantized data are correlated in the distributed system. The traditional methods to handle this joint problem tend to treat the detection and estimation tasks separately, which put more emphasis on the detection part but treat the estimation part sub-optimally. In this work, the joint detection and estimation fusion...
This paper explores a novel model to describe linear dynamic system with random delays. Compared with the existing research, the probabilities of random delays in the novel model are calculated by conditional probabilities. Therefore, the process noises and measurements noises in the new model for random delay problems are infinitely correlated. By treating the model as random parameter matrices Kalman...
In order to solve the problem that asynchronous multi-source multi-track cannot be correlated effectively, a trajectory similarity model for asynchronous multi-source multi-track and a track correlation algorithm based on this model are proposed in this paper. Based on the idea of searching potential matched data points under spatial and temporal constraints, the optimal matched point is determined...
Dempster-Shafer (D-S) evidence theory is widely used for information fusion field. However, one of the main issues of D-S evidence theory is that, when large amount of focal elements in Basic Probability Assignment (BPA) are available, the fusion of BPA requires high computational cost and long computing time. This problem greatly limits its application. In this paper, a novel method for approximating...
Information fusion aims to exploit truthful knowledge from various sources in a reliable and accurate way. Fusion of information can be conducted at three abstraction levels including feature level, score level and decision level. The feature fusion approaches have the advantages of preserving effective discriminative structure underlying various features. In this paper, we propose an effective feature...
We consider an infrastructure consisting of a network of systems each composed of discrete components that can be reinforced at a certain cost to guard against attacks. The network provides the vital connectivity between systems, and hence plays a critical, asymmetric role in the infrastructure operations. We characterize the system-level correlations using the aggregate failure correlation function...
In decentralised estimation, locally measured data are processed locally and the local filters are unaware of the other ones. Due to the lack of the global knowledge, the fusion of the local estimates cannot utilise the correlations of the local estimate errors in the computation of the fused mean square error matrix. For this reason, algorithms of fusion under unknown correlations have been designed...
Spatial outlier detection in wireless sensor network (WSN) can detect the objects whose non-spatial attributes are significantly different from their spatial neighbors, so as to ensure the reliability and accuracy of sensor data before decision-making process. The main drawback of existing spatial outlier detection algorithms is high user-dependency, which is not suitable for dynamic WSN data. This...
Estimation of periodic quantities such as angles or phase values is a common problem. However, standard approaches, for example the Kalman filter and extensions thereof, have difficulties when estimating periodic quantities. To address this problem, circular filtering algorithms have been proposed but they are limited to just a single angle. In order to deal with multiple, possibly correlated angles,...
Decentralized data fusion is a challenging task. Either it is too difficult to maintain and track the information required to perform fusion optimally, or too much information is discarded to obtain informative fusion results. A well-known solution is Covariance Intersection, which may provide too conservative fusion results. A less conservative alternative is discussed in this paper, and generalizations...
Due to the simplicity of its implementation and the impressive performance, Extreme Learning Machine (ELM) has been widely used in applications of machine learning. However, there are two potential problems in ELM: 1) lack of an efficient method for minimizing error; 2) consideration of little inherent structural information about correlations among output components. To overcome those problems, this...
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