The Coordinate Descent Method for K-means(CDKM)is an improved algorithm of K-means.It identifies better locally optimal solutions than the original K-means algorithm.That is,it achieves solutions that yield smaller ob...The Coordinate Descent Method for K-means(CDKM)is an improved algorithm of K-means.It identifies better locally optimal solutions than the original K-means algorithm.That is,it achieves solutions that yield smaller objective function values than the K-means algorithm.However,CDKM is sensitive to initialization,which makes the K-means objective function values not small enough.Since selecting suitable initial centers is not always possible,this paper proposes a novel algorithm by modifying the process of CDKM.The proposed algorithm first obtains the partition matrix by CDKM and then optimizes the partition matrix by designing the split-merge criterion to reduce the objective function value further.The split-merge criterion can minimize the objective function value as much as possible while ensuring that the number of clusters remains unchanged.The algorithm avoids the distance calculation in the traditional K-means algorithm because all the operations are completed only using the partition matrix.Experiments on ten UCI datasets show that the solution accuracy of the proposed algorithm,measured by the E value,is improved by 11.29%compared with CDKM and retains its efficiency advantage for the high dimensional datasets.The proposed algorithm can find a better locally optimal solution in comparison to other tested K-means improved algorithms in less run time.展开更多
The paper deals with state estimation problem of nonlinear non-Gaussian discrete dynamic systems for improvement of accuracy and consistency. An efficient new algorithm called the adaptive Gaussian-sum square-root cub...The paper deals with state estimation problem of nonlinear non-Gaussian discrete dynamic systems for improvement of accuracy and consistency. An efficient new algorithm called the adaptive Gaussian-sum square-root cubature Kalman filter（AGSSCKF） with a split-merge scheme is proposed. It is developed based on the squared-root extension of newly introduced cubature Kalman filter（SCKF） and is built within a Gaussian-sum framework. Based on the condition that the probability density functions of process noises and initial state are denoted by a Gaussian sum using optimization method, a bank of SCKF are used as the sub-filters to estimate state of system with the corresponding weights respectively, which is adaptively updated. The new algorithm consists of an adaptive splitting and merging procedure according to a proposed split-decision model based on the nonlinearity degree of measurement. The results of two simulation scenarios（one-dimensional state estimation and bearings-only tracking） show that the proposed filter demonstrates comparable performance to the particle filter with significantly reduced computational cost.展开更多
基金funded by National Defense Basic Research Program,grant number JCKY2019411B001funded by National Key Research and Development Program,grant number 2022YFC3601305funded by Key R&D Projects of Jilin Provincial Science and Technology Department,grant number 20210203218SF.
文摘The Coordinate Descent Method for K-means(CDKM)is an improved algorithm of K-means.It identifies better locally optimal solutions than the original K-means algorithm.That is,it achieves solutions that yield smaller objective function values than the K-means algorithm.However,CDKM is sensitive to initialization,which makes the K-means objective function values not small enough.Since selecting suitable initial centers is not always possible,this paper proposes a novel algorithm by modifying the process of CDKM.The proposed algorithm first obtains the partition matrix by CDKM and then optimizes the partition matrix by designing the split-merge criterion to reduce the objective function value further.The split-merge criterion can minimize the objective function value as much as possible while ensuring that the number of clusters remains unchanged.The algorithm avoids the distance calculation in the traditional K-means algorithm because all the operations are completed only using the partition matrix.Experiments on ten UCI datasets show that the solution accuracy of the proposed algorithm,measured by the E value,is improved by 11.29%compared with CDKM and retains its efficiency advantage for the high dimensional datasets.The proposed algorithm can find a better locally optimal solution in comparison to other tested K-means improved algorithms in less run time.
基金supported by the National Natural Science Foundation of China(No. 61032001)Shandong Provincial Natural Science Foundation of China (No. ZR2012FQ004)
文摘The paper deals with state estimation problem of nonlinear non-Gaussian discrete dynamic systems for improvement of accuracy and consistency. An efficient new algorithm called the adaptive Gaussian-sum square-root cubature Kalman filter（AGSSCKF） with a split-merge scheme is proposed. It is developed based on the squared-root extension of newly introduced cubature Kalman filter（SCKF） and is built within a Gaussian-sum framework. Based on the condition that the probability density functions of process noises and initial state are denoted by a Gaussian sum using optimization method, a bank of SCKF are used as the sub-filters to estimate state of system with the corresponding weights respectively, which is adaptively updated. The new algorithm consists of an adaptive splitting and merging procedure according to a proposed split-decision model based on the nonlinearity degree of measurement. The results of two simulation scenarios（one-dimensional state estimation and bearings-only tracking） show that the proposed filter demonstrates comparable performance to the particle filter with significantly reduced computational cost.
