An Improved Sparse Representation Classifier Based on Data Augmentation for Time Series Classification

Abstract

Sparse Representation-based Classification (SRC), which has achieved good performance in face recognition and other image classification, has been successfully extended to time series classification in recent years. As a generalization of the nearest subspace classifier, the performance of SRC depends on a rich set of training samples for each class, which can span as many variations of each class as possible under testing conditions. However, due to the difficulty of sample collection, many important applications can only provide a few or even a single sample for each class, which inevitably affects the performance of SRC. To address the problem of insufficient training samples, ESRC (Extended SRC) was proposed to put some samples that reflect the variations within the class into the dictionary, to obtain better generalization ability than SRC for under-sampled face recognition. In this paper, a new approach IESRC (Improved extend sparse representation based classification) is developed by splitting the dictionary into an original dictionary and auxiliary dictionary. The proposed model was first evaluated in 30 baseline data sets in the University of California, Riverside time series classification archive and then applied to classify the effects of aerobic exercise intervention in 24 young hypertensive patients based on the time series of the cardiopulmonary exercise test. Experimental results show that the proposed model can achieve better performance than SRC and ESRC in time series classification.