Characterization of Signals from Multiscale Edges
IEEE Transactions on Pattern Analysis and Machine Intelligence
Time-frequency analysis: theory and applications
Time-frequency analysis: theory and applications
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Transformation Invariance in Pattern Recognition-Tangent Distance and Tangent Propagation
Neural Networks: Tricks of the Trade, this book is an outgrowth of a 1996 NIPS workshop
Wearable and automotive systems for affect recognition from physiology
Wearable and automotive systems for affect recognition from physiology
Recognition with Local Features: the Kernel Recipe
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
A survey of kernels for structured data
ACM SIGKDD Explorations Newsletter
Automatic extraction of time-frequency skeletons with minimal spanning trees
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 01
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
Wavelet-based statistical signal processing using hidden Markovmodels
IEEE Transactions on Signal Processing
De-noising by soft-thresholding
IEEE Transactions on Information Theory
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Non-stationary signal classification is a complex problem. This problem becomes even more difficult if we add the following hypothesis: each signal includes a discriminant waveform, the time location of which is random and unknown. This is a problem that may arise in Brain Computer Interfaces (BCI) or in electroencephalogram recordings of patients prone to epilepsy. The aim of this article is to provide a new graph-based representation for classifying this kind of signals. This representation characterizes the waveform without reference to the absolute time location of the pattern in the signal. We will show that it is possible to create such a signal description using graphs on a time-scale or time-frequency signal representation. The definition of an inner product between graphs is then required to implement kernel methods algorithms like Support Vector Machines. Our experimental results shows that this approach is very promising and performs very well on real-world datasets.