Robust Spectral Fuzzy Clustering of Multivariate Time Series with Applications to Electroencephalogram
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915588017750016 |
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| author | Ma, Ziling Talento, Mara Sherlin Sun, Ying Ombao, Hernando |
| author_facet | Ma, Ziling Talento, Mara Sherlin Sun, Ying Ombao, Hernando |
| contents | Clustering multivariate time series (MTS) is challenging due to non-stationary cross-dependencies, noise contamination, and gradual or overlapping state boundaries. We introduce a robust fuzzy clustering framework in the spectral domain that leverages Kendall's tau-based canonical coherence to extract frequency-specific monotonic relationships across variables. Our method takes advantage of dominant frequency-based cross-regional connectivity patterns to improve clustering accuracy while remaining resilient to outliers, making the approach broadly applicable to noisy, high-dimensional MTS. Each series is projected onto vectors generated from a spectral matrix specifically tailored to capture the underlying fuzzy partitions. Numerical experiments demonstrate the superiority of our framework over existing methods. As a flagship application, we analyze electroencephalogram recordings, where our approach uncovers frequency- and connectivity-specific markers of latent cognitive states such as alertness and drowsiness, revealing discriminative patterns and ambiguous transitions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_22861 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Robust Spectral Fuzzy Clustering of Multivariate Time Series with Applications to Electroencephalogram Ma, Ziling Talento, Mara Sherlin Sun, Ying Ombao, Hernando Applications Methodology Machine Learning Clustering multivariate time series (MTS) is challenging due to non-stationary cross-dependencies, noise contamination, and gradual or overlapping state boundaries. We introduce a robust fuzzy clustering framework in the spectral domain that leverages Kendall's tau-based canonical coherence to extract frequency-specific monotonic relationships across variables. Our method takes advantage of dominant frequency-based cross-regional connectivity patterns to improve clustering accuracy while remaining resilient to outliers, making the approach broadly applicable to noisy, high-dimensional MTS. Each series is projected onto vectors generated from a spectral matrix specifically tailored to capture the underlying fuzzy partitions. Numerical experiments demonstrate the superiority of our framework over existing methods. As a flagship application, we analyze electroencephalogram recordings, where our approach uncovers frequency- and connectivity-specific markers of latent cognitive states such as alertness and drowsiness, revealing discriminative patterns and ambiguous transitions. |
| title | Robust Spectral Fuzzy Clustering of Multivariate Time Series with Applications to Electroencephalogram |
| topic | Applications Methodology Machine Learning |
| url | https://arxiv.org/abs/2506.22861 |