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

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.