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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| Vydáno v: | EURASIP journal on advances in signal processing Ročník 2012; číslo 1; s. 1 - 20 |
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27.11.2012
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| ISSN: | 1687-6180, 1687-6180 |
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| 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. |
|---|---|
| AbstractList | 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. 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. |
| ArticleNumber | 250 |
| Author | Ghoraani, Behnaz Krishnan, Sridhar |
| Author_xml | – sequence: 1 givenname: Behnaz surname: Ghoraani fullname: Ghoraani, Behnaz email: bghoraani@ieee.org organization: Department of Chemical and Biomedical Engineering, Rochester Institute of Technology, Department of Electrical and Computer, 1154 Engineering, Ryerson University – sequence: 2 givenname: Sridhar surname: Krishnan fullname: Krishnan, Sridhar organization: Department of Electrical and Computer, 1154 Engineering, Ryerson University |
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| Copyright | Ghoraani and Krishnan; licensee Springer. 2012. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
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| Keywords | Discriminant cluster selection means clustering The self-organizing tree map (SOTM) Time–frequency feature analysis Supervised classification Unsupervised clustering |
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Electrophysiol doi: 10.1111/j.1540-8167.2005.40708.x – volume: 92 start-page: 541 year: 2002 ident: 394_CR26 publication-title: J. Appl. Physiol doi: 10.1152/japplphysiol.00592.2001 – volume: 401 start-page: 788 issue: 6755 year: 1999 ident: 394_CR13 publication-title: Nature doi: 10.1038/44565 – start-page: 182 volume-title: Proceedings of the Second Joint EMBS/BMES Conference, vol. 1 year: 2002 ident: 394_CR24 – ident: 394_CR32 doi: 10.1186/1475-925X-6-23 – volume: 11 start-page: 341 year: 1982 ident: 394_CR2 publication-title: Int. J. Comput. Inf. Sci doi: 10.1007/BF01001956 – volume: 47 start-page: 773 issue: 6 year: 2000 ident: 394_CR23 publication-title: IEEE Trans. Biomed. Eng doi: 10.1109/10.844228 – volume: 1 start-page: 27 year: 2006 ident: 394_CR12 publication-title: IEEE Comput. Intell. Mag doi: 10.1109/MCI.2006.1626492 |
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| Title | Discriminant non-stationary signal features’ clustering using hard and fuzzy cluster labeling |
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