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Abstract Details
Activity Number:
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451
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Type:
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Topic Contributed
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Date/Time:
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Wednesday, August 3, 2011 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistical Learning and Data Mining
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Abstract - #300641 |
Title:
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Principal Subspace Estimation in Spiked Covariance Models
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Author(s):
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Zongming Ma*+
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Companies:
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University of Pennsylvania
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Address:
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Department of Statistics, Philadelphia, PA, 19104,
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Keywords:
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high dimension ;
principal component analysis ;
sparsity ;
spiked covariance model ;
subspace
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Abstract:
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For high-dimensional data, it is often desirable to reduce the dimensionality by projection onto a low dimensional principal subspace. However, classical PCA usually cannot find the subspace consistently in high dimensions. In this talk, we present a new principal subspace estimation method. For a class of spiked covariance models with sparsity constraints, it consistently, and even optimally, estimates the subspace.
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The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.
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