Indian Institute of Technology, Kanpur
Multi-scale Classification using Localized Spatial Depth
In this talk, we shall discuss the idea of depth based classification and develop a classifier using spatial depth. The construction of the proposed classifier is based on fitting a generalized additive model to the posterior probabilities corresponding to different classes. In order to cope with possible multi-modal as well as non-elliptic nature of the population distributions, we develop a localized version of spatial depth and use that with varying degrees of localization to build a collection of classifiers. Final classification is done by aggregating over several posterior probability estimates, each of which is based on localized spatial depth with a fixed level of localization. The new classifier can be conveniently used for high-dimensional data, and its good discriminatory power for such data has been established using theoretical and some numerical results.
This is a joint work with Soham Sarkar and Anil K. Ghosh.
Refreshments at 3:45pm in Snedecor 2101.