Inductive Geometric Matrix Midranges⁎This work has received support from the European Research Council under the Advanced ERC Grant Agreement Switchlet n.670645. Graham Van Goffrier acknowledges support from the Cambridge Mathematics Placement (CMP) Programme. Cyrus Mostajeran is supported by the Cambridge Philosophical Society. Issue 9 (2021)

• Record Type: Journal Article
• Authors: Van Goffrier, Graham W.; Mostajeran, Cyrus; Sepulchre, Rodolphe
• Is Part Of: IFAC-PapersOnLine. Volume 54:Issue 9(2021)
• Page Start: 584
• Page End: 589
• Publication Date: 2021
• Subjects: Classification -- Clustering -- Covariance Matrices -- Differential Geometry; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2021.06.120
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: Covariance 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.