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Krasnow Institute > Monday Seminars > Abstracts
On the role of the conditionality principle in dimensionality reduction Carey Priebe Department of Applied Mathematics and Statistics & Department of Computer Science & Center for Imaging Science, Johns Hopkins University The idea of classifier construction via `Iterative Denoising' trees--- that is, by successively partitioning (at the internal nodes of the tree) a class-labeled training data set into ever-more homogeneous subsets without consideration of class labels, and only subsequently (at the leaves of the tree) using the available class-label information, while at each node (internal or leaf) choosing a dimensionality reduction appropriate to and specific to (the data falling in) that partition cell --- may seem counter-intuitive but is in fact in (rough) accordance with Fisher's conditionality principle and can in fact provide performance superior to that of competing approaches. We describe the theory and application of these `Iterative Denoising' trees and illustrate their performance, and relate the ideas to `Integrated Sensing and Processing' and theorized thalamocortical brain circuit computation.
The Krasnow Institute for Advanced Study |
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