2018 journal article

Metric Driven Classification: A Non-Parametric Approach Based on the Henze-Penrose Test Statistic

IEEE TRANSACTIONS ON IMAGE PROCESSING, 27(12), 5947–5956.

By: S. Ghanem n, H. Krim n, H. Clouse & W. Sakla*

co-author countries: United States of America πŸ‡ΊπŸ‡Έ
author keywords: Dimensionality reduction; classification; divergence measures; nearest neighbor graph; pattern recognition
Source: Web Of Science
Added: October 16, 2018

Entropy-based divergence measures have proven their effectiveness in many areas of computer vision and pattern recognition. However, the complexity of their implementation might be prohibitive in resource-limited applications, as they require estimates of probability densities which are expensive to compute directly for high-dimensional data. In this paper, we investigate the usage of a non-parametric distribution-free metric, known as the Henze-Penrose test statistic to obtain bounds for the $k$ -nearest neighbors ( $k$ -NN) classification accuracy. Simulation results demonstrate the effectiveness and the reliability of this metric in estimating the inter-class separability. In addition, the proposed bounds on the $k$ -NN classification are exploited for evaluating the efficacy of different pre-processing techniques as well as selecting the least number of features that would achieve the desired classification performance.