Invariant subspace learning for time series data based on dynamic time warping distance
作者：
Highlights：
• A novel invariant subspace learning framework under DTW is proposed, which jointly solves the subspace learning and the alignment of multiple sequence samples. To the best of our knowledge, this is one of the first algorithms to explore the intrinsic subspace based on DTW distance, instead of Euclidean distance.
• The mutual promotion relationship between multiple sequence alignment and subspace learning is investigated and discussed.
• Experimental results show that the proposed subspace representation outperforms the state-of-the-art distance-based and feature-based methods in classification tasks.
摘要
•A novel invariant subspace learning framework under DTW is proposed, which jointly solves the subspace learning and the alignment of multiple sequence samples. To the best of our knowledge, this is one of the first algorithms to explore the intrinsic subspace based on DTW distance, instead of Euclidean distance.•The mutual promotion relationship between multiple sequence alignment and subspace learning is investigated and discussed.•Experimental results show that the proposed subspace representation outperforms the state-of-the-art distance-based and feature-based methods in classification tasks.
论文关键词：Invariant subspace learning,Dynamic time warping (DTW),Time series,Dictionary learning
论文评审过程：Received 11 May 2019, Revised 3 January 2020, Accepted 16 January 2020, Available online 17 January 2020, Version of Record 28 January 2020.
论文官网地址：https://doi.org/10.1016/j.patcog.2020.107210
