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SODA
2003
ACM
142views Algorithms» more  SODA 2003»
13 years 9 months ago
The similarity metric
—A new class of distances appropriate for measuring similarity relations between sequences, say one type of similarity per distance, is studied. We propose a new “normalized in...
Ming Li, Xin Chen, Xin Li, Bin Ma, Paul M. B. Vit&...
SODA
2001
ACM
87views Algorithms» more  SODA 2001»
13 years 9 months ago
Steiner points in tree metrics don't (really) help
Consider an edge-weighted tree T = (V, E, w : E R+ ), in which a subset R of the nodes (called the required nodes) are colored red and the remaining nodes in S = V \R are colored ...
Anupam Gupta
CCCG
2007
13 years 9 months ago
Approximating k-hop Minimum Spanning Trees in Euclidean Metrics
In the minimum-cost k-hop spanning tree (k-hop MST) problem, we are given a set S of n points in a metric space, a positive small integer k and a root point r ∈ S. We are intere...
Sören Laue, Domagoj Matijevic
ICML
2007
IEEE
14 years 8 months ago
Information-theoretic metric learning
In this paper, we present an information-theoretic approach to learning a Mahalanobis distance function. We formulate the problem as that of minimizing the differential relative e...
Jason V. Davis, Brian Kulis, Prateek Jain, Suvrit ...
KDD
2006
ACM
142views Data Mining» more  KDD 2006»
14 years 8 months ago
Mining distance-based outliers from large databases in any metric space
Let R be a set of objects. An object o R is an outlier, if there exist less than k objects in R whose distances to o are at most r. The values of k, r, and the distance metric ar...
Yufei Tao, Xiaokui Xiao, Shuigeng Zhou