Class-wise deep dictionaries for EEG classification

In this work we propose a classification framework called class-wise deep dictionary learning (CWDDL). For each class, multiple levels of dictionaries are learnt using features from the previous level as inputs (for first level the input is the raw training sample). It is assumed that the cascaded d...

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Bibliographic Details
Published in:2016 International Joint Conference on Neural Networks (IJCNN) pp. 3556 - 3563
Main Authors: Khurana, Prerna, Majumdar, Angshul, Ward, Rabab
Format: Conference Proceeding
Language:English
Published: IEEE 01.07.2016
Subjects:
Benchmark testing
deep learning
Dictionaries
dictionary learning
EEG
Machine learning
Minimization
Neural networks
Sparse matrices
Training
ISSN:2161-4407
Online Access:Get full text
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Summary:In this work we propose a classification framework called class-wise deep dictionary learning (CWDDL). For each class, multiple levels of dictionaries are learnt using features from the previous level as inputs (for first level the input is the raw training sample). It is assumed that the cascaded dictionaries form a basis for expressing test samples for that class. Based on this assumption sparse representation based classification is employed. Benchmarking experiments have been carried out on some deep learning datasets (MNIST and its variations, CIFAR and SVHN); our proposed method has been compared with Deep Belief Network (DBN), Stacked Autoencoder, Convolutional Neural Net (CNN) and Label Consistent KSVD (dictionary learning). We find that our proposed method yields better results than these techniques and requires much smaller run-times. The technique is applied for Brain Computer Interface (BCI) classification problems using EEG signals. For this problem our method performs significantly better than Convolutional Deep Belief Network(CDBN).
ISSN:2161-4407
DOI:10.1109/IJCNN.2016.7727656