k-nearest neighbor classification using dissimilarity increments

Authors:
Helena Aidos;Ana Fred
Affiliations:
Instituto de Telecomunicações, Instituto Superior Técnico, Lisboa, Portugal;Instituto de Telecomunicações, Instituto Superior Técnico, Lisboa, Portugal
Venue:
ICIAR'12 Proceedings of the 9th international conference on Image Analysis and Recognition - Volume Part I
Year:
2012

Citing 6
Cited 0

Statistical Pattern Recognition: A Review

IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Learning of Finite Mixture Models

IEEE Transactions on Pattern Analysis and Machine Intelligence
A New Cluster Isolation Criterion Based on Dissimilarity Increments

IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)
Pattern Recognition, Fourth Edition

Pattern Recognition, Fourth Edition
Statistical modeling of dissimilarity increments for d-dimensional data: Application in partitional clustering

Pattern Recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper we propose a classification method that generalizes the k-nearest neighbor (k-NN) rule in a maximum a posteriori (MAP) approach, using an additional characterization of the datasets. That characterization consists of a high order dissimilarity called dissimilarity increment; this dissimilarity measure uses information from three points at a time, unlike typical distances which are pairwise measures. In practice, in this model, the likelihood of a point not only depends of its direct k neighbors, but also of the nearest neighbor of each one of its k neighbors. Experimental results show that the proposed classifier outperforms more traditional and simple classifiers like Naive Bayes and k-nearest neighbor classifiers. This improved performance is especially noticeable relative to k-NN when k is poorly chosen.