Tensor decompositions are becoming increasingly important in processing images and videos. Previous methods, such as ANDECOMP/PARAFAC decomposition (CPD), Tucker decomposition (TKD), or tensor train decomposition (TTD), treat individual modes (or …
Continuous and factor explanatory variables are both important in linear regressions. To fit a linear model using factor variables, the traditional implementation of the least squares approach defines a number of dummy variables. However, this …
Tensor decomposition techniques are becoming increasingly important in processing videos with large sizes and dimensions. Under the framework of CANDECOMP/PARAFAC decomposition (CPD), this work studies low-rank sparse tensor approximations (LRSTAs) …
This work proposed a new approach called regression PCA (RegPCA) for statistical machine learning and big data analyses. One of the potential use cases investigated in this work is to separate the moving objects (foreground) from the background …
Categorical variables are nominal variables that classify observations by groups. The treatment of categorical variables in regression is a well-studied yet vital problem, with the most popular solution to perform a one hot encoding. However, …
This project aims at innovating big data computing algorithms.
Localization under GPS shadowed areas is an important yet challenging task for field robot operation. In this study, we propose a novel visual localization method for field robots. Our method leverages triangulation views to accurately locate the …