Sparse Representation for Waveforms Classification

Waveforms classification is an important task in many applications such as disease diagnosis, earthquake prediction and speech recognition. In this paper, a sparse representation based method is proposed for waveforms classification. Firstly, K singular value decomposition (K-SVD) method is applied to each class of training samples to obtain a corresponding dictionary. Then, for a test sample, it is sparsely represented and reconstructed by each dictionary respectively, and assign it to the class with the smallest reconstruction error. To verify the classification ability of the proposed method, two experiments on both simulated and real-world data sets are conducted. The final experimental results demonstrate that our proposed method can obtain a good performance in terms of the classification accuracy and noise tolerance.