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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 35 / No. 8: AAAI-21 Technical Tracks 8

Correlative Channel-Aware Fusion for Multi-View Time Series Classification

February 1, 2023

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Authors

Yue Bai

Northeastern University


Lichen Wang

Northeastern University


Zhiqiang Tao

Santa Clara University


Sheng Li

University of Georgia


Yun Fu

Northeastern University


DOI:

10.1609/aaai.v35i8.16830


Abstract:

Multi-view time series classification (MVTSC) aims to improve the performance by fusing the distinctive temporal information from multiple views. Existing methods for MVTSC mainly aim to fuse multi-view information at an early stage, e.g., by extracting a common feature subspace among multiple views. However, these approaches may not fully explore the unique temporal patterns of each view in complicated time series. Additionally, the label correlations of multiple views, which are critical to boosting, are usually under-explored for the MVTSC problem. To address the aforementioned issues, we propose a Correlative Channel-Aware Fusion (C$^2$AF) network. First, C$^2$AF extracts comprehensive and robust temporal patterns by a two-stream structured encoder for each view, and derives the intra-view/inter-view label correlations with a concise correlation matrix. Second, a channel-aware learnable fusion mechanism is implemented through CNN to further explore the global correlative patterns. Our C$^2$AF is an end-to-end framework for MVTSC. Extensive experimental results on three real-world datasets demonstrate the superiority of our C$^2$AF over the state-of-the-art methods. A detailed ablation study is also provided to illustrate the indispensability of each model component.

Topics: AAAI

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HOW TO CITE:

Yue Bai||Lichen Wang||Zhiqiang Tao||Sheng Li||Yun Fu Correlative Channel-Aware Fusion for Multi-View Time Series Classification Proceedings of the AAAI Conference on Artificial Intelligence (2021) 6714-6722.

Yue Bai||Lichen Wang||Zhiqiang Tao||Sheng Li||Yun Fu Correlative Channel-Aware Fusion for Multi-View Time Series Classification AAAI 2021, 6714-6722.

Yue Bai||Lichen Wang||Zhiqiang Tao||Sheng Li||Yun Fu (2021). Correlative Channel-Aware Fusion for Multi-View Time Series Classification. Proceedings of the AAAI Conference on Artificial Intelligence, 6714-6722.

Yue Bai||Lichen Wang||Zhiqiang Tao||Sheng Li||Yun Fu. Correlative Channel-Aware Fusion for Multi-View Time Series Classification. Proceedings of the AAAI Conference on Artificial Intelligence 2021 p.6714-6722.

Yue Bai||Lichen Wang||Zhiqiang Tao||Sheng Li||Yun Fu. 2021. Correlative Channel-Aware Fusion for Multi-View Time Series Classification. "Proceedings of the AAAI Conference on Artificial Intelligence". 6714-6722.

Yue Bai||Lichen Wang||Zhiqiang Tao||Sheng Li||Yun Fu. (2021) "Correlative Channel-Aware Fusion for Multi-View Time Series Classification", Proceedings of the AAAI Conference on Artificial Intelligence, p.6714-6722

Yue Bai||Lichen Wang||Zhiqiang Tao||Sheng Li||Yun Fu, "Correlative Channel-Aware Fusion for Multi-View Time Series Classification", AAAI, p.6714-6722, 2021.

Yue Bai||Lichen Wang||Zhiqiang Tao||Sheng Li||Yun Fu. "Correlative Channel-Aware Fusion for Multi-View Time Series Classification". Proceedings of the AAAI Conference on Artificial Intelligence, 2021, p.6714-6722.

Yue Bai||Lichen Wang||Zhiqiang Tao||Sheng Li||Yun Fu. "Correlative Channel-Aware Fusion for Multi-View Time Series Classification". Proceedings of the AAAI Conference on Artificial Intelligence, (2021): 6714-6722.

Yue Bai||Lichen Wang||Zhiqiang Tao||Sheng Li||Yun Fu. Correlative Channel-Aware Fusion for Multi-View Time Series Classification. AAAI[Internet]. 2021[cited 2023]; 6714-6722.


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
Copyright 2022, Association for the Advancement of
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