LDA/SVM Driven Nearest Neighbor Classification
Document Type
Article
Publication Date
7-1-2003
Abstract
Nearest neighbor (NN) classification relies on the assumption that class conditional probabilities are locally constant. This assumption becomes false in high dimensions with finite samples due to the curse of dimensionality. The NN rule introduces severe bias under these conditions. We propose a locally adaptive neighborhood morphing classification method to try to minimize bias. We use local support vector machine learning to estimate an effective metric for producing neighborhoods that are elongated along less discriminant feature dimensions and constricted along most discriminant ones. As a result, the class conditional probabilities can be expected to be approximately constant in the modified neighborhoods, whereby better classification performance can be achieved. The efficacy of our method is validated and compared against other competing techniques using a number of datasets.
DOI
10.1109/TNN.2003.813835
Montclair State University Digital Commons Citation
Peng, Jing; Heisterkamp, Douglas R.; and Dai, Ho Kwok, "LDA/SVM Driven Nearest Neighbor Classification" (2003). Department of Computer Science Faculty Scholarship and Creative Works. 369.
https://digitalcommons.montclair.edu/compusci-facpubs/369