2015/02/17 by Brijnesh J. Jain, Brijnesh Jain, Jain, Brijnesh
Computer Science · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Music and Audio Processing #Neural Networks and Applications #Time Series Analysis and Forecasting #cs.LG
paper · pdf · doi:10.48550/arxiv.1502.04843
accepted for publication in Machine Learning
openalex publication_date 2015/02/17 · arxiv created 2015/06/09 · arxiv updated 2015/06/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The majority of machine learning algorithms assumes that objects are represented as vectors. But often the objects we want to learn on are more naturally represented by other data structures such as sequences and time series. For these representations many standard learning algorithms are unavailable. We generalize gradient-based learning algorithms to time series under dynamic time warping. To this end, we introduce elastic functions, which extend functions on time series to matrix spaces. Necessary conditions are presented under which generalized gradient learning on time series is consistent. We indicate how results carry over to arbitrary elastic distance functions and to sequences consisting of symbolic elements. Specifically, four linear classifiers are extended to time series under dynamic time warping and applied to benchmark datasets. Results indicate that generalized gradient learning via elastic functions have the potential to complement the state-of-the-art in statistical pattern recognition on time series.