Improving the Computational Speed of a Tensor-Networked Kalman Filter for Streaming Video Completion

Streaming video completion is the practice that aims to fill in missing or corrupted pixels in a video stream by using past uncorrupted data. A method to tackle this problem is recently introduced called a Tensor Networked Kalman Filter (TNKF). It shows promising results in terms of performance compared to state-of-the-art methods for high...

Tensor-Networked Square-Root Kalman Filter for Online Video Completion

Online video completion aims to complete corrupted frames of a video in an online fashion. Consider a surveillance camera that suddenly outputs corrupted data, where up to 95% of the pixels per frame are corrupted. Real time video completion and correction is often desirable in such scenarios. Therefore, this thesis improves the Tensor-Networked...

Recursive Tensor Network Bayesian Learning of Large-Scale LS-SVMs

Least-squares support-vector-machines are a frequently used supervised learning method for nonlinear regression and classification. The method can be implemented by solving either its primal problem or dual problem. In the dual problem a linear system needs to be solved, yet for large-scale problems this can be impractical as current methods...