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PAMI
2006
117views more  PAMI 2006»
13 years 4 months ago
Metric Learning for Text Documents
High dimensional structured data such as text and images is often poorly understood and misrepresented in statistical modeling. The standard histogram representation suffers from ...
Guy Lebanon
FSTTCS
1993
Springer
13 years 9 months ago
Compact Location Problems
We investigate the complexity and approximability of some location problems when two distance values are specified for each pair of potential sites. These problems involve the se...
Venkatesh Radhakrishnan, Sven Oliver Krumke, Madha...
TALG
2008
81views more  TALG 2008»
13 years 4 months ago
Ordinal embeddings of minimum relaxation: General properties, trees, and ultrametrics
We introduce a new notion of embedding, called minimum-relaxation ordinal embedding, parallel to the standard notion of minimum-distortion (metric) embedding. In an ordinal embedd...
Noga Alon, Mihai Badoiu, Erik D. Demaine, Martin F...
TALG
2010
158views more  TALG 2010»
12 years 11 months ago
Clustering for metric and nonmetric distance measures
We study a generalization of the k-median problem with respect to an arbitrary dissimilarity measure D. Given a finite set P of size n, our goal is to find a set C of size k such t...
Marcel R. Ackermann, Johannes Blömer, Christi...
FOCS
1999
IEEE
13 years 9 months ago
Approximate Nearest Neighbor Algorithms for Hausdorff Metrics via Embeddings
Hausdorff metrics are used in geometric settings for measuring the distance between sets of points. They have been used extensively in areas such as computer vision, pattern recog...
Martin Farach-Colton, Piotr Indyk