123 citations · 366 across the 30 of their papers we have counts for
7 papers · 1 filter
A directed isoperimetric inequality with application to Bregman near neighbor lower bounds
Amirali Abdullah, Suresh Venkatasubramanian
Bregman divergences are a class of divergences parametrized by a convex function and include well known distance functions like and the Kullback-Leibler diverg…
Approximate Bregman near neighbors in sublinear time: Beyond the triangle inequality
Amirali Abdullah, John Moeller, Suresh Venkatasubramanian
In this paper we present the first provable approximate nearest-neighbor (ANN) algorithms for Bregman divergences. Our first algorithm processes queries in O(log^d n) time using O(…
A Gentle Introduction to the Kernel Distance
Jeff M. Phillips, Suresh Venkatasubramanian
This document reviews the definition of the kernel distance, providing a gentle introduction tailored to a reader with background in theoretical computer science, but limited expos…
Comparing Distributions and Shapes using the Kernel Distance
Sarang Joshi, Raj Varma Kommaraju, Jeff M. Phillips +1
Starting with a similarity function between objects, it is possible to define a distance metric on pairs of objects, and more generally on probability distributions over them. Thes…
Computing Hulls And Centerpoints In Positive Definite Space
P. Thomas Fletcher, John Moeller, Jeff M. Phillips +1
In this paper, we present algorithms for computing approximate hulls and centerpoints for collections of matrices in positive definite space. There are many applications where the…
The Hunting of the Bump: On Maximizing Statistical Discrepancy
Deepak Agarwal, Jeff M. Phillips, Suresh Venkatasubramanian
Anomaly detection has important applications in biosurveilance and environmental monitoring. When comparing measured data to data drawn from a baseline distribution, merely, findin…
Pattern Matching for sets of segments
Alon Efrat, Piotr Indyk, Suresh Venkatasubramanian
In this paper we present algorithms for a number of problems in geometric pattern matching where the input consist of a collections of segments in the plane. Our work consists of t…