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The whole and its parts: Visualizing gaussian mixture models

Visual Informatics 2024年第2期8卷 67-79页

作者： Joachim Giesen Philipp Lucas Linda Pfeiffer Laines Schmalwasser Kai Lawonn Friedrich-Schiller-University Jena Germany German Aerospace Center DLR-Institute of Data Science Jena Germany

gaussian mixture models are classical but still popular machine learning *** appealing feature of gaussian mixture models is their tractability,that is,they can be learned efficiently and exactly from data,and also support efficient exact inference queries like soft clustering data *** seemingly simple,gaussian mixture models can be hard to *** are at least four aspects to understanding gaussian mixture models,namely,understanding the whole distribution,its individual parts(mixture components),the relationships between the parts,and the interplay of the whole and its *** a structured literature review of applications of gaussian mixture models,we found the need for supporting all four *** identify candidate visualizations that effectively aid the user needs,we structure the available design space along three different representations of gaussian mixture models,namely as functions,sets of parameters,and sampling *** the design space,we implemented three design concepts that visualize the overall distribution together with its ***,we assessed the practical usefulness of the design concepts with respect to the different user needs in expert interviews and an insight-based user study.

关键词： gaussian mixture models

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A large-scale village classification model for tailored rural revitalization:A case study of Hubei province,China

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Journal of Geographical Sciences 2024年第12期34卷 2364-2392页

作者： PAN Yupiao ZHAO Xiang ZHANG Yiqing LUO Haifeng School of Resources and Environmental Sciences Wuhan UniversityWuhan 430079China

A comprehensive understanding of village development patterns and the identification of different village types is crucial for formulating tailored planning for rural ***,a model for large-scale village classification to support tailored rural revitalization planning is still *** study aims to develop a large-scale village classification model using the gaussian mixture models to support tailored rural revitalization ***,we propose a multi-dimensional index system to capture the diverse features of massive ***,the GMM clustering algorithm is applied to identify distinct village types based on their unique *** model was employed to classify the 25,409 villages in Hubei province in China into four *** in these classes exhibit discernible differences in spatial distribution,topography,location,economic development level,industrial structure,infrastructure,and resource *** addition,the GMM-based village classification model demonstrates a high level of agreement with evaluations made by planning experts,confirming its accuracy and *** the empirical study,our model achieves an overall accuracy of 95.29%,signifying substantial concordance between the classifications made by planning experts and the results generated by our *** on the identified features,tailored paths are proposed r each village class for rural revitalization efforts.

关键词： rural revitalization village classification gaussian mixture models rural regional development Hubei province

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Superiority of quadratic over conventional neural networks for classification of gaussian mixture data

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Visual Computing for Industry,Biomedicine,and Art 2022年第1期5卷 279-289页

作者： Tianrui Qi Ge Wang Department of Computer Science Rensselaer Polytechnic InstituteNY 12180TroyUSA

To enrich the diversity of artificial neurons,a type of quadratic neurons was proposed previously,where the inner product of inputs and weights is replaced by a quadratic *** this paper,we demonstrate the superiority of such quadratic neurons over conventional *** this purpose,we train such quadratic neural networks using an adapted backpropagation algorithm and perform a systematic comparison between quadratic and conventional neural networks for classificaiton of gaussian mixture data,which is one of the most important machine learning *** results show that quadratic neural networks enjoy remarkably better efficacy and efficiency than conventional neural networks in this context,and potentially extendable to other relevant applications.

关键词： Artificial neural networks Quadratic neurons Quadratic neural networks Backpropagation Classification gaussian mixture models

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A new constrained maximum margin approach to discriminative learning of Bayesian classifiers

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Frontiers of Information Technology & Electronic Engineering 2018年第5期19卷 639-650页

作者： Ke GUO Xia-bi LIU Lun-hao GUO Zong-jie LI Zeng-min GENG Beijing Laboratory of Intelligent Information Technology School of Computer ScienceBeijing Institute of Technology Computer Information Center Beijing Institute of Fashion Technology

We propose a novel discriminative learning approach for Bayesian pattern classification, called ＇constrained maximum margin （CMM）＇. We define the margin between two classes as the difference between the minimum decision value for positive samples and the maximum decision value for negative samples. The learning problem is to maximize the margin under the con- straint that each training pattern is classified correctly. This nonlinear programming problem is solved using the sequential un- constrained minimization technique. We applied the proposed CMM approach to learn Bayesian classifiers based on gaussian mixture models, and conducted the experiments on 10 UCI datasets. The performance of our approach was compared with those of the expectation-maximization algorithm, the support vector machine, and other state-of-the-art approaches. The experimental results demonstrated the effectiveness of our approach.

关键词： Discriminative learning Statistical modeling Bayesian pattern classifiers gaussian mixture models UCI datasets

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