We present a method for estimating eye gaze direction, which represents a departure from conventional eye gaze estimation methods, the majority of which are based on tracking spec...
We present a manifold learning approach to dimensionality
reduction that explicitly models the manifold as a mapping
from low to high dimensional space. The manifold is
represen...
We examine the problem of large scale nearest neighbor search in high dimensional spaces and propose a new approach based on the close relationship between nearest neighbor search...
In this paper, in order to reduce the explosive increase of the search space as the input dimension grows, we present a new representation method for the structure of fuzzy rules, ...
A model-constrained adaptive sampling methodology is proposed for reduction of large-scale systems with high-dimensional parametric input spaces. Our model reduction method uses a ...