Classification of multivariate time series using locality preserving projections

Abstract

Multivariate time series (MTS) are used in very broad areas such as multimedia, medicine, finance and speech recognition. A new approach for MTS classification using locality preserving projections (LPP) is proposed. By using LPP, the MTS samples can be projected into a lower-dimensional space in which the MTS samples related to the same class are close to each other, the MTS samples in testing set can be identified by one-nearest-neighbor classifier in the lower-dimensional space. Experimental results performed on five real-world datasets demonstrate the effectiveness of our proposed approach for MTS classification.