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FOCS
2006
IEEE
14 years 1 months ago
On the Optimality of the Dimensionality Reduction Method
We investigate the optimality of (1+ )-approximation algorithms obtained via the dimensionality reduction method. We show that: • Any data structure for the (1 + )-approximate n...
Alexandr Andoni, Piotr Indyk, Mihai Patrascu
KDD
2008
ACM
172views Data Mining» more  KDD 2008»
14 years 8 months ago
Structured metric learning for high dimensional problems
The success of popular algorithms such as k-means clustering or nearest neighbor searches depend on the assumption that the underlying distance functions reflect domain-specific n...
Jason V. Davis, Inderjit S. Dhillon
30
Voted
WWW
2009
ACM
14 years 8 months ago
Latent space domain transfer between high dimensional overlapping distributions
Transferring knowledge from one domain to another is challenging due to a number of reasons. Since both conditional and marginal distribution of the training data and test data ar...
Sihong Xie, Wei Fan, Jing Peng, Olivier Verscheure...
PAMI
2011
13 years 2 months ago
Approximate Nearest Subspace Search
—Subspaces offer convenient means of representing information in many pattern recognition, machine vision, and statistical learning applications. Contrary to the growing populari...
Ronen Basri, Tal Hassner, Lihi Zelnik-Manor
VLDB
1999
ACM
118views Database» more  VLDB 1999»
14 years 2 days ago
Similarity Search in High Dimensions via Hashing
The nearest- or near-neighbor query problems arise in a large variety of database applications, usually in the context of similarity searching. Of late, there has been increasing ...
Aristides Gionis, Piotr Indyk, Rajeev Motwani