Nearest Subspace with Discriminative Regularization for Time Series Classification

Abstract

For time series classification (TSC) problem, many studies focus on elastic distance measures for comparing time series and complete the task with the help of Nearest Neighbour (NN) classifier. This is mainly due to the fact that the order of variables is a crucial factor for time series. Unlike the NN classifier only considers one training sample, in this paper, we propose an improved Nearest Subspace (NS) classifier to classify new time series. By adding a discriminative regularization item, the improved NS classifier takes full advantage of all training time series of one class. Two kinds of discriminative regularization items are employed in our method. One is directly calculated based on Euclidean distance of time series. For the other, we obtain the regularization items from a lower-dimensional subspace. Two well-known dimensional reduction methods, Generalized Eigenvector Method (GEM) and Local Fisher Discriminant Analysis (LFDA), are employed to complete this task. Furthermore, we combine these improved NS classifiers through ensemble schemes to accommodate different time series datasets. Through extensive experiments on all UCR and UEA datasets, we demonstrate that the proposed method can gain better performance than NN classifiers with different elastic distance measures and other classifiers.