文章基本信息

标题：Inductive Geometric Matrix Midranges ⁎ ⁎
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作者：Graham W. Van Goffrier ; Cyrus Mostajeran ; Rodolphe Sepulchre 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2021
卷号：54
期号：9
页码：584-589
DOI：10.1016/j.ifacol.2021.06.120
语种：English
出版社：Elsevier
摘要：AbstractCovariance data as represented by symmetric positive definite (SPD) matrices are ubiquitous throughout technical study as efficient descriptors of interdependent systems. Euclidean analysis of SPD matrices, while computationally fast, can lead to skewed and even unphysical interpretations of data. Riemannian methods preserve the geometric structure of SPD data at the cost of expensive eigenvalue computations. In this paper, we propose a geometric method for unsupervised clustering of SPD data based on the Thompson metric. This technique relies upon a novel “inductive midrange” centroid computation for SPD data, whose properties are examined and numerically confirmed. We demonstrate the incorporation of the Thompson metric and inductive midrange into X-means and K-means++ clustering algorithms.
关键词：KeywordsClassificationClusteringCovariance MatricesDifferential Geometry