Abstract Details
Activity Number:
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301
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Type:
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Contributed
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Date/Time:
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Tuesday, August 5, 2014 : 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 #313788
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Title:
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WITHDRAWN: Interpreting Regularized Discriminant Analysis
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Author(s):
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John Ramey
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Companies:
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Keywords:
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regularized discriminant analysis ;
high-dimensional data ;
multivariate analysis ;
classification ;
supervised learning ;
machine learning
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Abstract:
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Regularized discriminant analysis (RDA) from Friedman (1989) is a classic supervised-learning method that utilizes a biased covariance-matrix estimator to partially pool the sample covariance matrices from linear and quadratic discriminant analysis before shrinking the resulting estimator towards a scaled identity matrix. Despite its popularity, the RDA classifier along with its two tuning parameters lack any clear interpretation and, as a result, have appeared merely ad hoc. However, after introducing a slight reparameterization, we derive the RDA classifier under a generative model and examine its properties. An interesting consequence of our derivation is that the resulting covariance-matrix estimator resembles one derived under both a contaminated Gaussian model and a finite Gaussian mixture model. We then discuss the derivation's further implications and outline future research opportunities after briefly applying the RDA classifier in simulations.
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Authors who are presenting talks have a * after their name.
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