In this paper, we propose a viewer for huge point-sampled models by combining out-of-core technologies with view-dependent level-of-detail (LOD) control. This viewer is designed o...
Mixtures of probabilistic principal component analyzers model high-dimensional nonlinear data by combining local linear models. Each mixture component is specifically designed to...
The k-means algorithm is widely used for clustering, compressing, and summarizing vector data. In this paper, we propose a new acceleration for exact k-means that gives the same a...
Topology preservation of Self-Organizing Maps (SOMs) is an advantageous property for correct clustering. Among several existing measures of topology violation, this paper studies t...
We propose a probabilistic model for cellular processes, and an algorithm for discovering them from gene expression data. A process is associated with a set of genes that particip...