Abstract— Meta-heuristic optimization approaches are commonly applied to many discrete optimization problems. Many of these optimization approaches are based on a local search op...
This paper considers a recently proposed method for unsupervised learning and dimensionality reduction, locally linear embedding (LLE). LLE computes a compact representation of hi...
We consider the general problem of learning from labeled and unlabeled data, which is often called semi-supervised learning or transductive inference. A principled approach to sem...
Dengyong Zhou, Olivier Bousquet, Thomas Navin Lal,...
Length and area regularization are commonplace for inverse problems today. It has however turned out to be much more difficult to incorporate a curvature prior. In this paper we pr...
Global mobile robot localization is the problem of determining a robot's pose in an environment, using sensor data, when the starting position is unknown. A family of probabi...