This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
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Activity Number:
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357
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Type:
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Contributed
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Date/Time:
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Tuesday, August 3, 2010 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistical Learning and Data Mining
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| Abstract - #308357 |
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Title:
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Embedding Directed Proximity Data
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Author(s):
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Minh Tang*+ and Michael Trosset
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Companies:
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Indiana University and Indiana University
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Address:
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409 S. Swain Ave, Bloomington, IN, 47401,
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Keywords:
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Multidimensional scaling ;
directed proximities ;
embedding
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Abstract:
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Multidimensional scaling (MDS) constructs Euclidean configurations of points from symmetric pairwise proximities, i.e., the edge weights of an undirected graph. In some applications, however, proximity is asymmetric, e.g., nearest neighbour graphs are directed. In such cases, one might symmetrize the proximity matrix and apply traditional MDS to the symmetrized proximities. Instead, we describe embedding techniques that constructs representation of directed proximity data.
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