Mean shift clustering is a powerful unsupervised data
analysis technique which does not require prior knowledge
of the number of clusters, and does not constrain the shape
of th...
In this paper, a new kernel-based method for data visualization and dimensionality reduction is proposed. A reference point is considered corresponding to additional constraints ta...
Many planning and design problems can be characterized as optimal search over a constrained network of conditional choices with preferences. To draw upon the advanced methods of c...
We analyze the performance of a class of manifold-learning algorithms that find their output by minimizing a quadratic form under some normalization constraints. This class consis...
Yair Goldberg, Alon Zakai, Dan Kushnir, Yaacov Rit...
— In this paper, we present a method for extracting consistent foreground regions when multiple views of a scene are available. We propose a framework that automatically identiï¬...