The success of popular algorithms such as k-means clustering or nearest neighbor searches depend on the assumption that the underlying distance functions reflect domain-specific n...
Some online algorithms for linear classification model the uncertainty in their weights over the course of learning. Modeling the full covariance structure of the weights can prov...
Justin Ma, Alex Kulesza, Mark Dredze, Koby Crammer...
This paper addresses the challenging problem of rapidly searching and matching high-dimensional features for the applications of multimedia database retrieval and pattern recognit...
Efficient index construction in multidimensional data spaces is important for many knowledge discovery algorithms, because construction times typically must be amortized by perform...
High-dimensional data visualization is receiving increasing interest because of the growing abundance of highdimensional datasets. To understand such datasets, visualization of th...