Distance measures for signal processing and pattern recognition
Signal Processing
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Multirate systems and filter banks
Multirate systems and filter banks
Adapted wavelet analysis from theory to software
Adapted wavelet analysis from theory to software
Floating search methods in feature selection
Pattern Recognition Letters
Wavelets and subband coding
The nature of statistical learning theory
The nature of statistical learning theory
Local discriminant bases and their applications
Journal of Mathematical Imaging and Vision - Special issue on mathematical imaging
An introduction to genetic algorithms
An introduction to genetic algorithms
Neural networks for pattern recognition
Neural networks for pattern recognition
Feature Extraction From Wavelet Coefficients for Pattern Recognition Tasks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Hybrid wavelet-support vector classification of waveforms
Journal of Computational and Applied Mathematics
Genetic Programming for Feature Detection and Image Segmentation
Selected Papers from AISB Workshop on Evolutionary Computing
Local feature extraction and its applications using a library of bases
Local feature extraction and its applications using a library of bases
An information theoretic approach to content based image retrieval
An information theoretic approach to content based image retrieval
Theory and design of signal-adapted FIR paraunitary filter banks
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Local Discriminant Wavelet Packet Coordinates for Face Recognition
The Journal of Machine Learning Research
Efficient wavelet adaptation for hybrid wavelet-large margin classifiers
Pattern Recognition
Pattern Recognition
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In recent years, wavelet packets have proven their capabilities for dimensionality reduction in waveform recognition. A well-accepted scheme is the local discriminant bases (LDB) algorithm which relies on the best-basis paradigm. In this paper, we combine the LDB algorithm with signal-adapted filter banks based on the lattice structure to construct more powerful LDBs. Here, additionally to the conventional tree adjustment, we adapt the shape of the analyzing atoms to extract discriminatory information among signal classes. We apply our shape-adapted LDBs, which we also call morphological LDBs, for current tasks of biosignal processing, namely feature extraction in waveforms from audiology and electrocardiology. Against the background of these applications, we show that our morphological LDBs outperform LDBs based on a fixed dictionary. We also present results which seem to open new research perspectives in audiology.