Covariances in Computer Vision and Machine Learning

Discover the cutting-edge insights in "Covariances in Computer Vision and Machine Learning" by Hà Quang Minh, published by Springer International Publishing AG in 2017. This engaging paperback, spanning 156 pages, delves into the computational aspects of kernel methods applied to covariance matrices, with a particular emphasis on the Log-Euclidean distance. The book also explores the latest advancements in extending finite-dimensional covariance matrix representations to infinite-dimensional covariance operator representations through positive definite kernels. Perfect for researchers and practitioners alike, this title is a must-read for anyone interested in the intersection of computer vision and machine learning. Enhance your understanding of these essential concepts and elevate your work in the field.