We propose a family of clustering algorithms based on the maximization of dependence between the input variables and their cluster labels, as expressed by the Hilbert-Schmidt Inde...
Le Song, Alexander J. Smola, Arthur Gretton, Karst...
Modern data mining settings involve a combination of attributevalued descriptors over entities as well as specified relationships between these entities. We present an approach t...
M. Shahriar Hossain, Satish Tadepalli, Layne T. Wa...
We introduce a family of unsupervised algorithms, numerical taxonomy clustering, to simultaneously cluster data, and to learn a taxonomy that encodes the relationship between the ...
Ideally, one would like to perform image search using an intuitive and friendly approach. Many existing image search engines, however, present users with sets of images arranged i...
Novi Quadrianto, Kristian Kersting, Tinne Tuytelaa...
We present a novel approach for interactive view-dependent rendering of massive models. Our algorithm combines view-dependent simplification, occlusion culling, and out-of-core re...
Sung-Eui Yoon, Brian Salomon, Russell Gayle, Dines...