Enhancing information discriminant analysis: Feature extraction with linear statistical model and information-theoretic criteria

AbstractThe linear discriminant analysis (LDA) is one of popular means for linear feature extraction. It usually performs well when the global data structure is consistent with the local data structure. Other frequently-used approaches of feature extraction usually require linear, independence, or large sample condition. However, in real world applications, these assumptions are not always satisfied or cannot be tested. In this paper, we introduce an adaptive method, local kernel nonparametric discriminant analysis (LKNDA), which integrates conventional discriminant analysis with nonparametric statistics. LKNDA is adept in identifying both complex nonlinear structures and the ad hoc rule. Six simulation cases demonstrate that LKNDA have both parametric and nonparametric algorithm advantages and higher classification accuracy. Quartic unilateral kernel function may provide better robustness of prediction than other functions. LKNDA gives an alternative solution for discriminant cases of complex nonlinear feature extraction or unknown feature extraction. At last, the application of LKNDA in the complex feature extraction of financial market activities is proposed.

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Classification of Sleep Stages Using Multi-wavelet Time Frequency Entropy and LDA

Methods of Information in Medicine ◽

10.3414/me09-01-0054 ◽

2010 ◽

Vol 49 (03) ◽

pp. 230-237 ◽

Cited By ~ 44

Author(s):

K. Lweesy ◽

N. Khasawneh ◽

M. Fraiwan ◽

H. Wenz ◽

H. Dickhaus ◽

...

Keyword(s):

Feature Extraction ◽

Wavelet Transform ◽

Discriminant Analysis ◽

Sleep Stage ◽

Continuous Wavelet ◽

Eeg Signal ◽

Linear Discriminant ◽

Time Frequency ◽

Mother Wavelets ◽

Sleep Stage Scoring

Summary Background: The process of automatic sleep stage scoring consists of two major parts: feature extraction and classification. Features are normally extracted from the polysomno-graphic recordings, mainly electroencephalograph (EEG) signals. The EEG is considered a non-stationary signal which increases the complexity of the detection of different waves in it. Objectives: This work presents a new technique for automatic sleep stage scoring based on employing continuous wavelet transform (CWT) and linear discriminant analysis (LDA) using different mother wavelets to detect different waves embedded in the EEG signal. Methods: The use of different mother wave-lets increases the ability to detect waves in the EEG signal. The extracted features were formed based on CWT time frequency entropy using three mother wavelets, and the classification was performed using the linear discriminant analysis. Thirty-two data sets from the MIT-BIH database were used to evaluate the performance of the proposed method. Results: Features of a single EEG signal were extracted successfully based on the time frequency entropy using the continuous wavelet transform with three mother wavelets. The proposed method has shown to outperform the classification based on a CWT using a single mother wavelet. The accuracy was found to be 0.84, while the kappa coefficient was 0.78. Conclusions: This work has shown that wavelet time frequency entropy provides a powerful tool for feature extraction for the non-stationary EEG signal; the accuracy of the classification procedure improved when using multiple wavelets compared to the use of single wavelet time frequency entropy.

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Robust feature extraction via information theoretic learning

Proceedings of the 26th Annual International Conference on Machine Learning - ICML '09 ◽

10.1145/1553374.1553526 ◽

2009 ◽

Cited By ~ 78

Author(s):

Xiao-Tong Yuan ◽

Bao-Gang Hu

Keyword(s):

Feature Extraction ◽

Information Theoretic ◽

Information Theoretic Learning ◽

Robust Feature Extraction

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Image feature extraction via local tensor rank one discriminant analysis

Electronics Letters ◽

10.1049/el.2011.2873 ◽

2011 ◽

Vol 47 (24) ◽

pp. 1320

Author(s):

S.-S. Wu ◽

Z.-S. Wei ◽

J.-F. Lu ◽

J.-Y. Yang

Keyword(s):

Feature Extraction ◽

Discriminant Analysis ◽

Image Feature ◽

Tensor Rank ◽

Image Feature Extraction ◽

Rank One

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