Finding latent patterns in high dimensional data is an important research problem with numerous applications. Existing approaches can be summarized into 3 categories: feature selec...
We equate nonlinear dimensionality reduction (NLDR) to graph embedding with side information about the vertices, and derive a solution to either problem in the form of a kernel-ba...
In this paper we give approximation algorithms for several proximity problems in high dimensional spaces. In particular, we give the rst Las Vegas data structure for (1 + )-neares...
The similarity join is an important database primitive which has been successfully applied to speed up applications such as similarity search, data analysis and data mining. The s...
Emerging microprocessors offer unprecedented parallel computing capabilities and deeper memory hierarchies, increasing the importance of loop transformations in optimizing compile...