Classification of multivariate time series using supervised neighborhood preserving embedding

Multivariate time series (MTS) are used in very broad areas such as finance, medicine, multimedia and speech recognition. Most of existing approaches for MTS classification are not designed for preserving the within-class local structure of the MTS dataset. The within-class local structure is important when a classifier is used for classification. In this paper, a new feature extraction method for MTS classification based on supervised Neighborhood Preserving Embedding (NPE) is proposed. MTS samples are projected into the PCA (principal component analysis) subspace by throwing away the smallest principal components, and then, the MTS samples in the PCA subspace are projected into a lower-dimensional space by using supervised NPE. Four different classifiers are used in our experiment. Experimental results performed on six real-world datasets demonstrate the effectiveness of our proposed approach for MTS classification.