Non-negative tensor factorization workflow for time series biomedical data

Non-negative tensor factorization workflow for time series biomedical data

Summary: Non-negative tensor factorization (NTF) enables the extraction of a small number of latent components from high-dimensional biomedical data. However, NTF requires many steps, which is a hurdle to implementation. Here, we provide a protocol for TensorLyCV, an easy to run and reproducible NTF...

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Bibliographic Details
Main Authors: Koki Tsuyuzaki, Naoki Yoshida, Tetsuo Ishikawa, Yuki Goshima, Eiryo Kawakami
Format: Article
Language:English
Published: Elsevier 2023-09-01
Series:STAR Protocols
Subjects:
Bioinformatics
Health Sciences
Computer sciences
Online Access:http://www.sciencedirect.com/science/article/pii/S266616672300285X

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http://www.sciencedirect.com/science/article/pii/S266616672300285X

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