Unsupervised classification method for hyperspectral image combining PCA and Gaussian mixture model

2003 ◽  
Author(s):  
Hao Wu ◽  
Gangyao Kuang ◽  
Wenxian Yu
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Hyperspectral Image ◽  
Unsupervised Classification ◽  
Gaussian Mixture ◽  
Classification Method

scholarly journals Gaussian Mixture Model Based Classification Revisited: Application to the Bearing Fault Classification

2020 ◽  
Vol 66 (4) ◽  
pp. 215-226 ◽  
Author(s):  
Branislav Panić ◽  
Jernej Klemenc ◽  
Marko Nagode
Keyword(s):  
Fault Detection ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Condition Monitoring ◽  
Gaussian Mixture ◽  
Classification Method ◽  
Fault Classification ◽  
Classification Methods ◽  
Bearing Fault ◽  
Model Based

Condition monitoring and fault detection are nowadays popular topic. Different loads, enviroments etc. affect the components and systems differently and can induce the fault and faulty behaviour. Most of the approaches for the fault detection rely on the use of the good classification method. Gaussian mixture model based classification are stable and versatile methods which can be applied to a wide range of classification tasks. The main task is the estimation of the parameters in the Gaussian mixture model. Those can be estimated with various techniques. Therefore, the Gaussian mixture model based classification have different variants which can vary in performance. To test the performance of the Gaussian mixture model based classification variants and general usefulness of the Gaussian mixture model based classification for the fault detection, we have opted to use the bearing fault classification problem. Additionally, comparisons with other widely used non-parametric classification methods are made, such as support vector machines and neural networks. The performance of each classification method is evaluated by multiple repeated k-fold cross validation. From the results obtained, Gaussian mixture model based classification methods are shown to be competitive and efficient methods and usable in the field of fault detection and condition monitoring.

scholarly journals Resident user load classification method based on improved Gaussian mixture model clustering

2022 ◽  
Vol 355 ◽  
pp. 02024
Author(s):  
Haojing Wang ◽  
Yingjie Tian ◽  
An Li ◽  
Jihai Wu ◽  
Gaiping Sun
Keyword(s):  
Principal Component Analysis ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Clustering Algorithm ◽  
Computational Cost ◽  
Principal Component ◽  
Component Analysis ◽  
Gaussian Mixture ◽  
Classification Method ◽  
Mixture Model Clustering

In view of the limitation of “hard assignment” of clusters in traditional clustering methods and the difficulty of meeting the requirements of clustering efficiency and clustering accuracy simultaneously in regard to massive data sets, a load classification method based on a Gaussian mixture model combining clustering and principal component analysis is proposed. The load data are fed into a Gaussian mixture model clustering algorithm after principal component analysis and dimensionality reduction to achieve classification of large-scale load datasets. The method in this paper is used to classify loads in the Canadian AMPds2 public dataset and is compared with K-Means, Gaussian mixed model clustering and other methods. The results show that the proposed method can not only achieve load classification more effectively and finely, but also save computational cost and improve computational efficiency.

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