This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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573
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Type:
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Topic Contributed
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Date/Time:
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Wednesday, August 4, 2010 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistical Learning and Data Mining
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Abstract - #307382 |
Title:
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Accurate Parameter Estimation Using High-Dimensional Astrophysical Data
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Author(s):
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Joseph William Richards*+
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Companies:
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Carnegie Mellon University
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Address:
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5000 Forbes Avenue, Pittsburgh, PA, 15213,
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Keywords:
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manifold learning ;
diffusion map ;
dimension reduction ;
astrostatistics ;
basis learning
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Abstract:
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An important problem is how to properly analyze high-dimensional data that resides near a lower-dimensional manifold. This problem arises often in astrophysics, where, though data can be high-dimensional, the underlying models have only a few parameters. The diffusion map method is an effective means to analyze high-dimensional data with low-dimensional structure. The novelty of the diffusion map is that it creates a simple coordinate system in which Euclidean distances between data points accurately describe their degree of dissimilarity. Recently, we have used diffusion map for basis learning in a set of high-dimensional simple stellar population spectra. Particularly, we show how, for a database of galaxies, the diffusion map basis can be used along with astrophysical model fitting to obtain more accurate estimates of star formation history parameters than those in the literature.
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Authors who are presenting talks have a * after their name.
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