Discriminant non-stationary signal features’ clustering using hard and fuzzy cluster labeling

Behnaz Ghoraani,Sridhar Krishnan

doi:10.1186/1687-6180-2012-250

Abstract

Current approaches to improve the pattern recognition performance mainly focus on either extracting non-stationary and discriminant features of each class, or employing complex and nonlinear feature classifiers. However, little attention has been paid to the integration of these two approaches. Combining non-stationary feature analysis with complex feature classifiers, this article presents a novel direction to enhance the discriminatory power of pattern recognition methods. This approach, which is based on a fusion of non-stationary feature analysis with clustering techniques, proposes an algorithm to adaptively identify the feature vectors according to their importance in representing the patterns of discrimination. Non-stationary feature vectors are extracted using a non-stationary method based on time–frequency distribution and non-negative matrix factorization. The clustering algorithms including the K-means and self-organizing tree maps are utilized as unsupervised clustering methods followed by a supervised labeling. Two labeling methods are introduced: hard and fuzzy labeling. The article covers in detail the formulation of the proposed discriminant feature clustering method. Experiments performed with pathological speech classification, T-wave alternans evaluation from the surface electrocardiogram, audio scene analysis, and telemonitoring of Parkinson’s disease problems produced desirable results. The outcome demonstrates the benefits of non-stationary feature fusion with clustering methods for complex data analysis where existing approaches do not exhibit a high performance.

Highlights

The advancement in sensor technology made it possible to gather huge amounts of data, which on the one hand extends the applicability of signal analysis to a wide variety of fields, such as communications, security, biomedicine, biology, physics, finance, and geology
We adopt the notion of unsupervised clustering; unlike commonly used unsupervised clustering methods, we propose to perform the clustering stage on all the training feature vectors obtained from the different classes and train one set of clusters for the entire training features
We present a synthetic example of a two-class problem to demonstrate the identification process of signal classification using TF feature extraction and the proposed cluster selection method

Summary

Introduction

The advancement in sensor technology made it possible to gather huge amounts of data, which on the one hand extends the applicability of signal analysis to a wide variety of fields, such as communications, security, biomedicine, biology, physics, finance, and geology. In a signal classification method, a feature extraction divides a signal into short-duration segments and Supervised learning approaches are developed based on the assumption that the structures of signals from different classes are completely different. They find a discriminating pattern among signals by dividing the feature space into non-overlapping subspaces which represent each corresponding class. This approach might be satisfactory in cases the signals are separable in the feature space, this approach seems to be too optimistic in applications where an overlap exists between different classes. Nonstationarities in the real-world signals cause some variations in the signals’ properties which may result in spread and overlapping of the obtained feature vectors over the feature space

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