可扩展聚类：有异常值的高斯混杂模型的大规模无监督学习

IF 1.4 2区数学 Q2 STATISTICS & PROBABILITY

Journal of Computational and Graphical Statistics Pub Date : 2024-10-14 DOI:10.1080/10618600.2024.2414889

Yijia Zhou, Kyle A. Gallivan, Adrian Barbu

引用次数: 0

摘要

聚类是一种广泛应用的技术，在各个领域都有着悠久而丰富的历史。然而，现有的大多数算法不能很好地扩展到大型数据集，或者缺乏理论保证...

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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Scalable Clustering: Large Scale Unsupervised Learning of Gaussian Mixture Models with Outliers

Clustering is a widely used technique with a long and rich history in a variety of areas. However, most existing algorithms do not scale well to large datasets, or are missing theoretical guarantee...

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来源期刊

Journal of Computational and Graphical Statistics 数学-统计学与概率论

CiteScore

3.50

自引率

8.30%

发文量

153

审稿时长

>12 weeks

期刊介绍： The Journal of Computational and Graphical Statistics (JCGS) presents the very latest techniques on improving and extending the use of computational and graphical methods in statistics and data analysis. Established in 1992, this journal contains cutting-edge research, data, surveys, and more on numerical graphical displays and methods, and perception. Articles are written for readers who have a strong background in statistics but are not necessarily experts in computing. Published in March, June, September, and December.