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SDM
2011
SIAM
414views Data Mining» more  SDM 2011»
13 years 1 months ago
Clustered low rank approximation of graphs in information science applications
In this paper we present a fast and accurate procedure called clustered low rank matrix approximation for massive graphs. The procedure involves a fast clustering of the graph and...
Berkant Savas, Inderjit S. Dhillon
SIGMOD
2006
ACM
125views Database» more  SIGMOD 2006»
14 years 11 months ago
A non-linear dimensionality-reduction technique for fast similarity search in large databases
To enable efficient similarity search in large databases, many indexing techniques use a linear transformation scheme to reduce dimensions and allow fast approximation. In this re...
Khanh Vu, Kien A. Hua, Hao Cheng, Sheau-Dong Lang
STOC
2001
ACM
138views Algorithms» more  STOC 2001»
14 years 11 months ago
Fast computation of low rank matrix
Given a matrix A, it is often desirable to find a good approximation to A that has low rank. We introduce a simple technique for accelerating the computation of such approximation...
Dimitris Achlioptas, Frank McSherry
SODA
2010
ACM
175views Algorithms» more  SODA 2010»
14 years 8 months ago
Fast SDP Algorithms for Constraint Satisfaction Problems
The class of constraint satisfactions problems (CSPs) captures many fundamental combinatorial optimization problems such as Max Cut, Max q-Cut, Unique Games, and Max k-Sat. Recent...
David Steurer
CORR
2008
Springer
147views Education» more  CORR 2008»
13 years 11 months ago
C-K-R Partitions of Sparse Graphs
We present fast algorithms for constructing probabilistic embeddings and approximate distance oracles in sparse graphs. The main ingredient is a fast algorithm for sampling the pr...
Manor Mendel, Chaya Schwob