Robust and scalable manifold learning via landmark diffusion for long-term medical signal processing
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, Journal Article
Shen, C; Lin, Y-T; Wu, H-T
2020
Motivated by analyzing long-term physiological time series, we design a robust and scalable spectral embedding algorithm, coined the algorithm RObust and Scalable Embedding via LANdmark Diffusion (ROSE-LAND). The key is designing a diffusion process on the dataset, where the diffusion is forced to interchange on a small subset called the landmark set . In addition to demonstrating its application to spectral clustering and image segmentation, the algorithm is applied to study the long-term arterial blood pressure waveform dynamics during a liver transplant operation lasting for 12 hours long.
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Shen, C., Lin, Y.-T., & Wu, H.-T. (2020). Robust and scalable manifold learning via landmark diffusion for long-term medical signal processing. https://doi.org/10.1101/2020.05.31.126649
Shen, Chao, Yu-Ting Lin, and Hau-Tieng Wu. “Robust and scalable manifold learning via landmark diffusion for long-term medical signal processing,” 2020. https://doi.org/10.1101/2020.05.31.126649.
Shen C, Lin Y-T, Wu H-T. Robust and scalable manifold learning via landmark diffusion for long-term medical signal processing. 2020;
Shen, Chao, et al. Robust and scalable manifold learning via landmark diffusion for long-term medical signal processing. 2020. Epmc, doi:10.1101/2020.05.31.126649.
Shen C, Lin Y-T, Wu H-T. Robust and scalable manifold learning via landmark diffusion for long-term medical signal processing. 2020;