This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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532
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Type:
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Contributed
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Date/Time:
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Wednesday, August 4, 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 - #308528 |
Title:
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Fast Euclidean Embedding of Ordinal Nearest Neighbor Graphs
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Author(s):
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Brent Shannon Castle*+ and Faming Liang and Michael Trosset
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Companies:
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Indiana University and Texas A&M University and Indiana University
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Address:
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5347 N. College Ave. #315, Indianapolis, IN, 46220,
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Keywords:
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nonmetric multidimensional scaling ;
nearest neighbor graphs ;
raw stress criterion ;
partial ordering ;
isotonic regression
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Abstract:
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A number of interesting data sets provide information about the nearest neighbors of a set of objects. For example, each book sold by Amazon.com is accompanied by a list of its most similar neighbors, measured by the proportion of viewers who ultimately buy the other book. These data can be conceived as a directed graph in which vertices represent objects and edges identify nearest neighbor structure. For Amazon.com, the edges are weighted by a quantitative measure of proximity; in other applications, the edge weights may be ordinal, i.e., the ranks of the nearest neighbors may be provided without a corresponding quantitative measure of proximity. We develop methods for mapping such data sets, i.e., for embedding ordinal nearest neighbor graphs in low-dimensional Euclidean spaces.
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