The Experts below are selected from a list of 39216 Experts worldwide ranked by ideXlab platform
Jean Dezert - One of the best experts on this subject based on the ideXlab platform.
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a new belief based K Nearest Neighbor classification method
Pattern Recognition, 2013Co-Authors: Zhunga Liu, Quan Pan, Jean DezertAbstract:The K-Nearest Neighbor (K-NN) classification method originally developed in the probabilistic frameworK has serious difficulties to classify correctly the close data points (objects) originating from different classes. To cope with such difficult problem and maKe the classification result more robust to misclassification errors, we propose a new belief-based K-Nearest Neighbor (BK-NN) method that allows each object to belong both to the specific classes and to the sets of classes with different masses of belief. BK-NN is able to provide a hyper-credal classification on the specific classes, the rejection classes and the meta-classes as well. Thus, the objects hard to classify correctly are automatically committed to a meta-class or to a rejection class, which can reduce the misclassification errors. The basic belief assignment (bba) of each object is defined from the distance between the object and its Neighbors and from the acceptance and rejection thresholds. The bba's are combined using a new combination method specially developed for the BK-NN. Several experiments based on simulated and real data sets have been carried out to evaluate the performances of the BK-NN method with respect to several classical K-NN approaches.
Pramod Viswanath - One of the best experts on this subject based on the ideXlab platform.
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demystifying fixed K Nearest Neighbor information estimators
IEEE Transactions on Information Theory, 2018Co-Authors: Weihao Gao, Pramod ViswanathAbstract:Estimating mutual information from independent identically distributed samples drawn from an unKnown joint density function is a basic statistical problem of broad interest with multitudinous applications. The most popular estimator is the one proposed by KrasKov, Stogbauer, and Grassberger (KSG) in 2004 and is nonparametric and based on the distances of each sample to its $K^{\mathrm{ th}}$ Nearest Neighboring sample, where $K$ is a fixed small integer. Despite of its widespread use (part of scientific software pacKages), theoretical properties of this estimator have been largely unexplored. In this paper, we demonstrate that the estimator is consistent and also identify an upper bound on the rate of convergence of the $\ell _{2}$ error as a function of a number of samples. We argue that the performance benefits of the KSG estimator stems from a curious “correlation boosting” effect and build on this intuition to modify the KSG estimator in novel ways to construct a superior estimator. As a by-product of our investigations, we obtain nearly tight rates of convergence of the $\ell _{2}$ error of the well-Known fixed $K$ -Nearest Neighbor estimator of differential entropy by KozachenKo and LeonenKo.
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Demystifying fixed K-Nearest Neighbor information estimators
2017 IEEE International Symposium on Information Theory (ISIT), 2017Co-Authors: Sewoong Oh, Pramod ViswanathAbstract:Estimating mutual information from i.i.d. samples drawn from an unKnown joint density function is a basic statistical problem of broad interest with multitudinous applications. The most popular estimator is one proposed by KrasKov and Stogbauer and Grassberger (KSG) in 2004, and is nonparametric and based on the distances of each sample to its Kth Nearest Neighboring sample, where K is a fixed small integer. Despite its widespread use (part of scientific software pacKages), theoretical properties of this estimator have been largely unexplored. In this paper we demonstrate that the estimator is consistent and also identify an upper bound on the rate of convergence of the ℓ2 error as a function of number of samples. We argue that the performance benefits of the KSG estimator stems from a curious “correlation boosting” effect and build on this intuition to modify the KSG estimator in novel ways to construct a superior estimator. As a byproduct of our investigations, we obtain nearly tight rates of convergence of the ℓ2 error of the well Known fixed K Nearest Neighbor estimator of differential entropy by KozachenKo and LeonenKo.
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demystifying fixed K Nearest Neighbor information estimators
arXiv: Learning, 2016Co-Authors: Weihao Gao, Pramod ViswanathAbstract:Estimating mutual information from i.i.d. samples drawn from an unKnown joint density function is a basic statistical problem of broad interest with multitudinous applications. The most popular estimator is one proposed by KrasKov and Stogbauer and Grassberger (KSG) in 2004, and is nonparametric and based on the distances of each sample to its $K^{\rm th}$ Nearest Neighboring sample, where $K$ is a fixed small integer. Despite its widespread use (part of scientific software pacKages), theoretical properties of this estimator have been largely unexplored. In this paper we demonstrate that the estimator is consistent and also identify an upper bound on the rate of convergence of the bias as a function of number of samples. We argue that the superior performance benefits of the KSG estimator stems from a curious "correlation boosting" effect and build on this intuition to modify the KSG estimator in novel ways to construct a superior estimator. As a byproduct of our investigations, we obtain nearly tight rates of convergence of the $\ell_2$ error of the well Known fixed $K$ Nearest Neighbor estimator of differential entropy by KozachenKo and LeonenKo.
Changdong Wang - One of the best experts on this subject based on the ideXlab platform.
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a novel clustering method based on hybrid K Nearest Neighbor graph
Pattern Recognition, 2018Co-Authors: Yikun Qin, Changdong WangAbstract:Abstract Most of the existing clustering methods have difficulty in processing complex nonlinear data sets. To remedy this deficiency, in this paper, a novel data model termed Hybrid K-Nearest-Neighbor (HKNN) graph, which combines the advantages of mutual K-Nearest-Neighbor graph and K-Nearest-Neighbor graph, is proposed to represent the nonlinear data sets. Moreover, a Clustering method based on the HKNN graph (CHKNN) is proposed. The CHKNN first generates several tight and small subclusters, then merges these subclusters by exploiting the connectivity among them. In order to select the optimal parameters for CHKNN, we further propose an internal validity index termed K-Nearest-Neighbor Index (KNNI), which can also be used to evaluate the validity of nonlinear clustering results by varying a control parameter. Experimental results on synthetic and real-world data sets, as well as that on the video clustering, have demonstrated the significant improvement on performance over existing nonlinear clustering methods and internal validity indices.
Mutlu Avci - One of the best experts on this subject based on the ideXlab platform.
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a hybrid classification method of K Nearest Neighbor bayesian methods and genetic algorithm
Expert Systems With Applications, 2010Co-Authors: Mehmet Aci, Cigdem Inan, Mutlu AvciAbstract:K Nearest Neighbor, Bayesian methods and genetic algorithms are effective methods of machine learning. In this worK a hybrid method is formed by using these methods and algorithm together. The aim is to achieve successful results on classifying by eliminating data that maKe difficult to learn. Forming new data set approach is proposed according to good data on the hand. Test process is done with five of UCI machine learning datasets. These are iris, breast cancer, glass, yeast and wine data sets. Test results are investigated in collaboration with the previous worKs, and the success of the study is considered.
Piyush Kumar - One of the best experts on this subject based on the ideXlab platform.
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fast construction of K Nearest Neighbor graphs for point clouds
IEEE Transactions on Visualization and Computer Graphics, 2010Co-Authors: Michael Connor, Piyush KumarAbstract:We present a parallel algorithm for K-Nearest Neighbor graph construction that uses Morton ordering. Experiments show that our approach has the following advantages over existing methods: 1) faster construction of K-Nearest Neighbor graphs in practice on multicore machines, 2) less space usage, 3) better cache efficiency, 4) ability to handle large data sets, and 5) ease of parallelization and implementation. If the point set has a bounded expansion constant, our algorithm requires one-comparison-based parallel sort of points, according to Morton order plus near-linear additional steps to output the K-Nearest Neighbor graph.
