Radio map construction requires a large amount of radio measurement data with location
labels, which imposes a high deployment cost. This paper develops a region-based radio …

Modern data are increasingly both high-dimensional and heteroscedastic. This paper
considers the challenge of estimating underlying principal components from high …

The traditional construction of radio maps demands extensive radio measurement data
accompanied by precise location labels, thus necessitating considerable calibration. This …

When dealing with a parametric statistical model, a Riemannian manifold can naturally
appear by endowing the parameter space with the Fisher information metric. The geometry …

This paper deals with the Elliptical Wishart and Inverse Elliptical Wishart distributions, which
play a major role when handling covariance matrices. Similarly to multivariate elliptical …

Streaming principal component analysis (PCA) is an integral tool in large-scale machine
learning for rapidly estimating low-dimensional subspaces of very high dimensional and …

This paper deals with Elliptical Wishart distributions-which generalize the Wishart
distribution-in the context of signal processing and machine learning. Two algorithms to …

Mixtures of probabilistic principal component analysis (MPPCA) is a well-known mixture
model extension of principal component analysis (PCA). Similar to PCA, MPPCA assumes …

In this study, we consider the realm of covariance matrices in machine learning, particularly
focusing on computing Fr\'echet means on the manifold of symmetric positive definite …

Remote sensing systems offer an increased opportunity to record multi-temporal and
multidimensional images of the earth's surface. This opportunity greatly increases the …