On Recursive Edit Distance Kernels with Application to Time Series Classification

TL;DR

This paper introduces new recursive edit distance kernels for time series classification that are positive definite under weaker conditions, improving kernel performance when traditional distances are not positive definite.

Contribution

The authors develop novel recursive elastic kernels with weaker sufficient conditions for positive definiteness, enhancing time series classification methods.

Findings

Recursive elastic kernels outperform distance-based kernels when pairwise distances are indefinite.

Proposed kernels are positive definite under weaker conditions than previous methods.

Support Vector Machine classifiers achieve better accuracy with these kernels on time series data.

Abstract

This paper proposes some extensions to the work on kernels dedicated to string or time series global alignment based on the aggregation of scores obtained by local alignments. The extensions we propose allow to construct, from classical recursive definition of elastic distances, recursive edit distance (or time-warp) kernels that are positive definite if some sufficient conditions are satisfied. The sufficient conditions we end-up with are original and weaker than those proposed in earlier works, although a recursive regularizing term is required to get the proof of the positive definiteness as a direct consequence of the Haussler's convolution theorem. The classification experiment we conducted on three classical time warp distances (two of which being metrics), using Support Vector Machine classifier, leads to conclude that, when the pairwise distance matrix obtained from the training data is \textit{far} from definiteness, the positive definite recursive elastic kernels outperform in general the distance substituting kernels for the classical elastic distances we have tested.