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Surrogate Modelling for Injection Molding Processes using Machine Learning
Injection molding is one of the most popular manufacturing methods for the modeling of complex plastic objects.
Kernel Methods on Approximate Infinite-Dimensional Covariance Operators for Image Classification
This paper presents a novel framework for visual object recognition using infinite-dimensional covariance operators of input features in the paradigm of kernel methods on infinite-dimensional Riemannian manifolds.
Approximate Log-Hilbert-Schmidt Distances Between Covariance Operators for Image Classification
This paper presents a novel framework for visual object recognition using infinite-dimensional covariance operators of input features, in the paradigm of kernel methods on infinite-dimensional Riemannian manifolds.
Kernelized Covariance for Action Recognition
In this paper we aim at increasing the descriptive power of the covariance matrix, limited in capturing linear mutual dependencies between variables only.
Log-Hilbert-Schmidt metric between positive definite operators on Hilbert spaces
This paper introduces a novel mathematical and computational framework, namely {\it Log-Hilbert-Schmidt metric} between positive definite operators on a Hilbert space.
