Inductive Geometric Matrix Midranges
This work addresses the need for more accurate and computationally efficient clustering of SPD data in fields like machine learning and signal processing, representing an incremental advancement over existing Riemannian methods.
The paper tackles the problem of clustering symmetric positive definite (SPD) matrices by proposing a geometric method based on the Thompson metric and a novel 'inductive midrange' centroid computation, which is integrated into X-means and K-means++ algorithms to improve accuracy and efficiency.
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.