Responsible Gaussian Model: Matrix-Based Approximation of Gaussian Mixture Model

IF 3.6 3区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Wataru Obayashi;Takeru Aoki;Tomoaki Tatsukawa
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引用次数: 0

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.
负责任高斯模型:基于矩阵的高斯混合模型逼近
深度学习的机制通常被视为一个不明确的结构,很难用数学或工程原理精确地解释或控制。相比之下,统计学与机器学习有着相似的理论基础和应用领域,但提供了更多可解释的模型和数学上严格的框架。然而,统计方法在处理难以分析的问题时面临局限性。高斯混合模型(GMM)是将正态分布相加得到的一种分布。此外,它是一个灵活的概率分布。它非常实用,但难以分析解决;但是,可以采用迭代方法,如EM算法来求解。在本研究中,我们提出了一个新的模型,我们提出了一个新的替代高斯混合模型的负责任高斯模型,以避免迭代解和获得解析近似。为了评估其性能,我们将该模型应用于鸢尾植物数据集、葡萄酒识别数据集、手写数字光学识别数据集和CIFAR-10数据集上。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
IEEE Access
IEEE Access COMPUTER 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.
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