Learning Prototype Models for Tangent Distance
Trevor Hastie, Patrice Simard · 1994
Simard, LeCun & Denker #1993# showed that the performance of near-neighbor classi#cation schemes for handwritten character recognition can be improved by incorporating invariance to speci #c transformations in the underlying distance metric --- the so called tangent distance. The resulting classi#er, however, can be prohibitively slow and memory intensive due to the large amountof prototypes that need to be stored and used in the distance comparisons. In this paper we develop rich models for representing large subsets of the prototypes. These models are either used singly per class, or as basic building blocks in conjunction with the K-means clustering algorithm. # After September 1, 1994: Statistics Department, Sequoia Hall, Stanford University, CA94305. Email: [email protected] 1 INTRODUCTION Local algorithms such as K-nearest neighbor #NN# perform well in pattern recognition, even though they often assume the simplest distance on the pattern space. It has re...