Abstract Details
Activity Number:
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409
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Type:
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Contributed
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Date/Time:
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Tuesday, August 5, 2014 : 2:00 PM to 2:45 PM
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Sponsor:
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Section on Nonparametric Statistics
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Abstract #314057
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Title:
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Noise Estimation in High-Dimensional PCA
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Author(s):
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Didier Chetelat*+
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Companies:
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Cornell University
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Keywords:
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principal components analysis ;
PCA ;
covariance estimation ;
unbiased risk estimator ;
high dimension
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Abstract:
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A popular model in high-dimensional Principal Components Analysis (PCA) is the spiked model, where a few eigenvalues are substantial while the others equal some small noise parameter. We propose to estimate this noise by variational optimization on the unbiased risk estimator of a natural loss. The resulting estimator turns out to possess a closed form solution and excellent theoretical behavior. To showcase performance, we use it to build a high-dimensional covariance estimator with substantial gains under spikedness, yet robustness against the assumption.
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Authors who are presenting talks have a * after their name.
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