A parametric classification rule based on the exponentially embedded family
Document Type
Article
Date of Original Version
2-1-2015
Abstract
In this paper, we extend the exponentially embedded family (EEF), a new approach to model order estimation and probability density function construction originally proposed by Kay in 2005, to multivariate pattern recognition. Specifically, a parametric classifier rule based on the EEF is developed, in which we construct a distribution for each class based on a reference distribution. The proposed method can address different types of classification problems in either a data-driven manner or a model-driven manner. In this paper, we demonstrate its effectiveness with examples of synthetic data classification and real-life data classification in a data-driven manner and the example of power quality disturbance classification in a model-driven manner. To evaluate the classification performance of our approach, the Monte-Carlo method is used in our experiments. The promising experimental results indicate many potential applications of the proposed method.
Publication Title, e.g., Journal
IEEE Transactions on Neural Networks and Learning Systems
Volume
26
Issue
2
Citation/Publisher Attribution
Tang, Bo, Haibo He, Quan Ding, and Steven Kay. "A parametric classification rule based on the exponentially embedded family." IEEE Transactions on Neural Networks and Learning Systems 26, 2 (2015): 367-377. doi: 10.1109/TNNLS.2014.2383692.