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O(log s)-Approximate Nearest Neighbor Search for Earth Mover's Distance

Offered By: Simons Institute via YouTube

Tags

Sublinear Algorithms Courses Data Structures Courses Locality-Sensitive Hashing Courses Approximation Algorithms Courses Computational Geometry Courses

Course Description

Overview

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Explore a groundbreaking lecture on improving approximation for nearest neighbor search under the Earth Mover's Distance (EMD). Delve into the first advancement in this field in over a decade, presented by Rajesh Jayaram from Google Research NYC. Learn about the EMD metric, its significance in computer science, and its application in comparing sets of points. Discover the innovative approach to achieving an Õ(log s) approximation with sublinear query time, improving upon previous O(log^2 s) results. Gain insights into data-dependent locality sensitive hash functions for EMD, tree embedding techniques, and the key concepts behind this improved algorithm. Understand the implications of this research for sublinear algorithms and its potential impact on various applications of EMD in computer science.

Syllabus

O(log s)-Approximate Nearest Neighbor Search for the Earth Mover’s Distance


Taught by

Simons Institute

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