no code implementations • 24 Apr 2022 • Xiaoqiang Hua, Yusuke Ono, Linyu Peng, Yuting Xu
We define a projection that maps the HPD matrices in a high-dimensional manifold to a low-dimensional and more discriminative one to increase the degree of separation of HPD matrices by maximizing the data variance.
no code implementations • 27 May 2021 • Xiaoqiang Hua, Linyu Peng
As customary, the sample data is assumed to be modeled as Hermitian positive-definite (HPD) matrices, and the geometric median of a set of HPD matrices is interpreted as an estimate of the clutter covariance matrix (CCM).
no code implementations • 27 Dec 2020 • Xiaoqiang Hua, Yusuke Ono, Linyu Peng, Yongqiang Cheng, Hongqiang Wang
Information divergences are commonly used to measure the dissimilarity of two elements on a statistical manifold.
