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Activity Number: 301
Type: Contributed
Date/Time: Tuesday, August 5, 2014 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Learning and Data Mining
Abstract #313788
Title: WITHDRAWN: Interpreting Regularized Discriminant Analysis
Author(s): John Ramey
Companies:
Keywords: regularized discriminant analysis ; high-dimensional data ; multivariate analysis ; classification ; supervised learning ; machine learning
Abstract:

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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