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. Comb...

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Veröffentlicht in:EURASIP journal on advances in signal processing Jg. 2012; H. 1; S. 1 - 20
Hauptverfasser: Ghoraani, Behnaz, Krishnan, Sridhar
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Cham Springer International Publishing 27.11.2012
Schlagworte:
Advances in Nonstationary Electrophysiological Signal Analysis and Processing
Algorithms
Classifiers
Clustering
Engineering
Feature extraction
Fuzzy
Labels
Marking
Pattern recognition
Quantum Information Technology
Signal,Image and Speech Processing
Spintronics
Discriminant cluster selection
means clustering
The self-organizing tree map (SOTM)
Time–frequency feature analysis
Supervised classification
Unsupervised clustering
ISSN:1687-6180, 1687-6180
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Zusammenfassung: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.
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ISSN:1687-6180
1687-6180
DOI:10.1186/1687-6180-2012-250