Modal clustering of matrix-variate data
Open Access
- 5 May 2022
- journal article
- research article
- Published by Springer Science and Business Media LLC in Advances in Data Analysis and Classification
- Vol. 17 (2), 323-345
- https://doi.org/10.1007/s11634-022-00501-x
Abstract
The nonparametric formulation of density-based clustering, known as modal clustering, draws a correspondence between groups and the attraction domains of the modes of the density function underlying the data. Its probabilistic foundation allows for a natural, yet not trivial, generalization of the approach to the matrix-valued setting, increasingly widespread, for example, in longitudinal and multivariate spatio-temporal studies. In this work we introduce nonparametric estimators of matrix-variate distributions based on kernel methods, and analyze their asymptotic properties. Additionally, we propose a generalization of the mean-shift procedure for the identification of the modes of the estimated density. Given the intrinsic high dimensionality of matrix-variate data, we discuss some locally adaptive solutions to handle the problem. We test the procedure via extensive simulations, also with respect to some competitors, and illustrate its performance through two high-dimensional real data applications.Keywords
This publication has 38 references indexed in Scilit:
- On matrix-variate regression analysisJournal of Multivariate Analysis, 2012
- Model based clustering for three-way data structuresBayesian Analysis, 2011
- Comparative study on classifying human activities with miniature inertial and magnetic sensorsPattern Recognition, 2010
- Feature significance for multivariate kernel density estimationComputational Statistics & Data Analysis, 2008
- A hierarchical mixture model for clustering three-way data setsComputational Statistics & Data Analysis, 2007
- The Discrete Cosine TransformSIAM Review, 1999
- Silhouettes: A graphical aid to the interpretation and validation of cluster analysisJournal of Computational and Applied Mathematics, 1987
- The mixture method of clustering applied to three-way dataJournal of Classification, 1985
- A fast cosine transform in one and two dimensionsIEEE Transactions on Acoustics, Speech, and Signal Processing, 1980
- The estimation of the gradient of a density function, with applications in pattern recognitionIEEE Transactions on Information Theory, 1975