Robust and scalable manifold learning via landmark diffusion for long-term medical signal processing

Lin, Y.-T.; Wu, H.-T.; Shen, C.

doi:10.1101/2020.05.31.126649

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bioRxiv · 10.1101/2020.05.31.126649

Robust and scalable manifold learning via landmark diffusion for long-term medical signal processing

Lin, Y.-T.·Wu, H.-T.·Shen, C.

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Abstract

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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Lin, Y.-T., Wu, H.-T., Shen, C.. 2020-06-01. Robust and scalable manifold learning via landmark diffusion for long-term medical signal processing. https://doi.org/10.1101/2020.05.31.126649

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