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Self-adaptive Gaussian mixture reduction method based on variational Bayesian

A technique of variational Bayesian and Gaussian mixture, applied in the field of information fusion

Pending Publication Date: 2021-03-16
NORTHWESTERN POLYTECHNICAL UNIV
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Problems solved by technology

This method overcomes the limitation of relying only on Euclidean distance for classification basis in the traditional Gaussian mixture reduction problem, and is suitable for supervised learning of multimedia data, data fusion, pattern recognition, target detection and tracking, nonlinear filter design and other fields

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  • Self-adaptive Gaussian mixture reduction method based on variational Bayesian
  • Self-adaptive Gaussian mixture reduction method based on variational Bayesian
  • Self-adaptive Gaussian mixture reduction method based on variational Bayesian

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Embodiment Construction

[0096] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0097] The invention provides an adaptive Gaussian mixture reduction method based on variational Bayesian. The method first performs weight detection and pruning, and then uses the VariationalBayes Expectation Maxim-ization (VBEM) algorithm to realize unsupervised clustering on the basis of establishing many Gaussian components as a Gaussian mixture model, and finally uses the StandardMixture Merging (SMM) algorithm Merge between classes. By applying the classical k-means, EM and VBEM algorithms to the clustering of Gaussian mixture components, this method realizes a reasonable Gaussian mixture reduction process for the Gaussian components that increase exponentially during the fusion process, making it possible for the clustering of Gaussian mixture components with excessive quantities in the process The reduced final approximate form with many Gaussi...

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Abstract

The invention discloses a self-adaptive Gaussian mixture reduction method based on variational Bayesian. Firstly, weight detection and pruning are carried out, then numerous Gaussian components are established into a Gaussian mixture model, unsupervised clustering is achieved through a VBEM algorithm, finally, inter-class combination is carried out through an SMM algorithm, self-adaptive Gaussianmixture reduction is completed, the simulation effect is compared with traditional k-means and EM, and a good result is obtained. The method overcomes the limitation that the traditional Gaussian mixture reduction problem only depends on the Euclidean distance to perform classification basis, and is suitable for the fields of supervised learning, data fusion, pattern recognition, target detectiontracking, nonlinear filter design and the like of multimedia data.

Description

technical field [0001] The invention belongs to the technical field of information fusion, and in particular relates to a Gaussian mixture reduction method. Background technique [0002] In many practical applications, such as: supervised learning of multimedia data, data fusion, pattern recognition, target detection and tracking, nonlinear filter design, Gaussian mixture is an important probability representation of the system. However, a problem arises during data processing that the number of Gaussian components increases exponentially with time. Therefore, the research of Gaussian mixture reduction algorithm is particularly important. [0003] The document "Xu Y, Fang Y, Peng W, et al. An efficient Gaussian Sum Filter based on Prune-Cluster-Merge Scheme [J]. IEEE Access, 2019, PP(99): 1-1." proposed a method based on A Gaussian mixture reduction method for the Prune-Cluster-Merge (PCM) scheme. The method first adopts an adaptive weight pruning strategy to remove Gauss...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06F18/23213G06F18/251
Inventor 李天成唐欣怡杨峰郑丽涛
Owner NORTHWESTERN POLYTECHNICAL UNIV
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