'Est neighbor' presentation slideshows

CS 361A (Advanced Data Structures and Algorithms)

CS 361A (Advanced Data Structures and Algorithms). Lecture 19 (Dec 5, 2005) Nearest Neighbors: Dimensionality Reduction and Locality-Sensitive Hashing Rajeev Motwani. Metric Space. Metric Space (M,D) For points p,q in M , D(p,q) is distance from p to q

569 views • 46 slides

Locality Sensitive Hashing

341 views • 33 slides

Geometric Problems in High Dimensions: Sketching

Geometric Problems in High Dimensions: Sketching. Piotr Indyk. High Dimensions. We have seen several algorithms for low-dimensional problems (d=2, to be specific): data structure for orthogonal range queries (kd-tree) data structure for approximate nearest neighbor (kd-tree)

249 views • 16 slides

Geometric Problems in High Dimensions: Sketching

Geometric Problems in High Dimensions: Sketching. Piotr Indyk. High Dimensions. We have seen several algorithms for low-dimensional problems (d=2, to be specific): data structure for orthogonal range queries (kd-tree) data structure for approximate nearest neighbor (kd-tree)

172 views • 16 slides

Near(est) Neighbor in High Dimensions

Alexandr Andoni (slides by Piotr Indyk). Near(est) Neighbor in High Dimensions. Nearest Neighbor. Given: A set P of points in R d Goal: build data structure which, for any query q, returns a point p ? P minimizing ||p-q||. q. Solution for d=2 (sketch). Compute Voronoi diagram

188 views • 10 slides