{"title":"负责任高斯模型:基于矩阵的高斯混合模型逼近","authors":"Wataru Obayashi;Takeru Aoki;Tomoaki Tatsukawa","doi":"10.1109/ACCESS.2025.3604006","DOIUrl":null,"url":null,"abstract":"Mechanisms of deep learning are often viewed as a unclear structure and are difficult to interpret or control precisely using mathematical or engineering principles. In contrast, statistics shares similar theoretical foundations and application areas with machine learning but offers more interpretable models and mathematically rigorous frameworks. However, statistical methods face limitations when dealing with analytically intractable problems. The Gaussian mixture model (GMM) is a distribution obtained by adding normal distributions. In addition, it is a flexible probability distribution. It is highly practical but difficult to solve analytically; however, iterative methods, such as EM algorithm, can be applied to solve it. In this study, we propose the model with a new model, we propose The Responsible Gaussian Model a novel alternative to the Gaussian Mixture Model to avoid iterative solutions and obtain analytical approximation. To evaluate its performance, we applied the model to clustering on Iris plants dataset, Wine recognition dataset, Optical recognition of Handwritten digits dataset, and CIFAR-10.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"154295-154304"},"PeriodicalIF":3.6000,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11145024","citationCount":"0","resultStr":"{\"title\":\"Responsible Gaussian Model: Matrix-Based Approximation of Gaussian Mixture Model\",\"authors\":\"Wataru Obayashi;Takeru Aoki;Tomoaki Tatsukawa\",\"doi\":\"10.1109/ACCESS.2025.3604006\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Mechanisms of deep learning are often viewed as a unclear structure and are difficult to interpret or control precisely using mathematical or engineering principles. In contrast, statistics shares similar theoretical foundations and application areas with machine learning but offers more interpretable models and mathematically rigorous frameworks. However, statistical methods face limitations when dealing with analytically intractable problems. The Gaussian mixture model (GMM) is a distribution obtained by adding normal distributions. In addition, it is a flexible probability distribution. It is highly practical but difficult to solve analytically; however, iterative methods, such as EM algorithm, can be applied to solve it. In this study, we propose the model with a new model, we propose The Responsible Gaussian Model a novel alternative to the Gaussian Mixture Model to avoid iterative solutions and obtain analytical approximation. To evaluate its performance, we applied the model to clustering on Iris plants dataset, Wine recognition dataset, Optical recognition of Handwritten digits dataset, and CIFAR-10.\",\"PeriodicalId\":13079,\"journal\":{\"name\":\"IEEE Access\",\"volume\":\"13 \",\"pages\":\"154295-154304\"},\"PeriodicalIF\":3.6000,\"publicationDate\":\"2025-08-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11145024\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Access\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/11145024/\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Access","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/11145024/","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
Responsible Gaussian Model: Matrix-Based Approximation of Gaussian Mixture Model
Mechanisms of deep learning are often viewed as a unclear structure and are difficult to interpret or control precisely using mathematical or engineering principles. In contrast, statistics shares similar theoretical foundations and application areas with machine learning but offers more interpretable models and mathematically rigorous frameworks. However, statistical methods face limitations when dealing with analytically intractable problems. The Gaussian mixture model (GMM) is a distribution obtained by adding normal distributions. In addition, it is a flexible probability distribution. It is highly practical but difficult to solve analytically; however, iterative methods, such as EM algorithm, can be applied to solve it. In this study, we propose the model with a new model, we propose The Responsible Gaussian Model a novel alternative to the Gaussian Mixture Model to avoid iterative solutions and obtain analytical approximation. To evaluate its performance, we applied the model to clustering on Iris plants dataset, Wine recognition dataset, Optical recognition of Handwritten digits dataset, and CIFAR-10.
IEEE AccessCOMPUTER SCIENCE, INFORMATION SYSTEMSENGIN-ENGINEERING, ELECTRICAL & ELECTRONIC
CiteScore
9.80
自引率
7.70%
发文量
6673
审稿时长
6 weeks
期刊介绍:
IEEE Access® is a multidisciplinary, open access (OA), applications-oriented, all-electronic archival journal that continuously presents the results of original research or development across all of IEEE''s fields of interest.
IEEE Access will publish articles that are of high interest to readers, original, technically correct, and clearly presented. Supported by author publication charges (APC), its hallmarks are a rapid peer review and publication process with open access to all readers. Unlike IEEE''s traditional Transactions or Journals, reviews are "binary", in that reviewers will either Accept or Reject an article in the form it is submitted in order to achieve rapid turnaround. Especially encouraged are submissions on:
Multidisciplinary topics, or applications-oriented articles and negative results that do not fit within the scope of IEEE''s traditional journals.
Practical articles discussing new experiments or measurement techniques, interesting solutions to engineering.
Development of new or improved fabrication or manufacturing techniques.
Reviews or survey articles of new or evolving fields oriented to assist others in understanding the new area.