The paper presents an approach to hierarchical clustering based on the use of a least general generalization (lgg) operator to induce a lattice structure of clusters and a categor...
With hierarchical clustering methods, divisions or fusions, once made, are irrevocable. As a result, when two elements in a bottom-up algorithm are assigned to one cluster, they c...
We introduce a boosting framework to solve a classification problem with added manifold and ambient regularization costs. It allows for a natural extension of boosting into both s...
Nicolas Loeff, David A. Forsyth, Deepak Ramachandr...
Feature selection plays a fundamental role in many pattern
recognition problems. However, most efforts have been
focused on the supervised scenario, while unsupervised feature
s...
Bin Zhao, James Tin-Yau Kwok, Fei Wang, Changshui ...
The application of semi-supervised learning algorithms to large scale vision problems suffers from the bad scaling behavior of most methods. Based on the Expectation Regularization...