Least Squares Support Vector Machine Classifiers
Neural Processing Letters
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Benchmarking Least Squares Support Vector Machine Classifiers
Machine Learning
Feature extraction approaches based on matrix pattern: MatPCA and MatFLDA
Pattern Recognition Letters
Boosting the distance estimation
Pattern Recognition Letters
2D Direct LDA Algorithm for Face Recognition
SERA '06 Proceedings of the Fourth International Conference on Software Engineering Research, Management and Applications
Fast k-nearest-neighbor search based on projection and triangular inequality
Pattern Recognition
A window width optimized S-transform
EURASIP Journal on Advances in Signal Processing
A note on two-dimensional linear discriminant analysis
Pattern Recognition Letters
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
An extension of the naive Bayesian classifier
Information Sciences: an International Journal
2D-LDA: A statistical linear discriminant analysis for image matrix
Pattern Recognition Letters
Localization of the complex spectrum: the S transform
IEEE Transactions on Signal Processing
The S-Transform and Its Inverses: Side Effects of Discretizing and Filtering
IEEE Transactions on Signal Processing
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
BDPCA plus LDA: a novel fast feature extraction technique for face recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A neuro-fuzzy approach to gear system monitoring
IEEE Transactions on Fuzzy Systems
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Time-frequency representations (TFR) have been intensively employed for analyzing vibration signals in mechanical faults diagnosis. However, in many applications, time-frequency representations are simply utilized as a visual aid to be used for vibration signal analysis. It is very attractive to investigate the utility of TFR for automatic classification of vibration signals. A key step for this work is to extract discriminative parameters from TFR as input feature vector for classifiers. This paper contributes to this ongoing investigation by developing a two direction two dimensional linear discriminative analysis (TD-2DLDA) technique for feature extraction from TFR. The S transform, which combines the separate strengths of the short time Fourier transform and wavelet transforms, is chosen to perform the time-frequency analysis of vibration signals. Then, a novel feature extraction technique, named TD-2DLDA, is proposed to represent the time-frequency matrix. As opposed to traditional LDA, TD-2DLDA is directly conduct on 2D matrices rather than 1D vectors, so the time-frequency matrix does not need to be transformed into a vector prior to feature extraction. Therefore, the TD-2DLDA can reduce the computation cost and preserve more structure information hiding in original 2D matrices compared to the LDA. The promise of our method is illustrated by performing our procedure on vibration signals measured from a gearbox with five operating states. Experimental results indicate that the TD-2DLDA obviously outperforms related feature extraction schemes such as LDA, 2DLDA in gear fault diagnosis.