| Document Date: 2012-06-12 10:11:42
Open Document File Size: 454,84 KBShare Result on Facebook
City New York / Edinburgh / / Company IBM / Google / Intel / Facebook / / Country United States / United Kingdom / Scotland / / / Facility Columbia University / / IndustryTerm approximate optimization algorithm / sub-linear hashing/search time / search performance / point-to-point nearest neighbor search / point-to-hyperplane search / point-to-hyperplane search problem / nearest subspace search / approximate nearest neighbor search / search accuracy / point-to-point search / point-to-point search problem / search problem / reasonable search accuracy / large scale search / attractive solution / search speed / exhaustive search / reduced search speed / approximation algorithms / subspace-to-subspace nearest neighbor search / point-topoint search / / Organization idf / U.S. Securities and Exchange Commission / AP (MAP) / Columbia University / New York / / Person Mean / Wei Liu† Jun Wang / Williamson / Yadong Mu† Sanjiv Kumar / Shih-Fu Chang / / Position author / hB / first author / randomized functions hB / randomized bilinear hash function hB / Pr hB / / Product Pentax K-x Digital Camera / / ProvinceOrState New York / / PublishedMedium Journal of the ACM / Machine Learning / / Technology RAM / classical AL algorithm / Machine Learning / approximate optimization algorithm / D. P. Improved approximation algorithms / BH-Hash algorithm / / URL http / |
Compact Hyperplane Hashing with Bilinear Functions Wei Liu† Jun Wang‡