Visualization of incrementally learned projection trajectories for longitudinal data

Abstract Longitudinal studies that continuously generate data enable the capture of temporal variations in experimentally observed parameters, facilitating the interpretation of results in a time-aware manner. We propose IL-VIS (incrementally learned visualizer), a new machine learning pipeline that...

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Главные авторы:	Tamasha Malepathirana, Damith Senanayake, Vini Gautam, Martin Engel, Rachelle Balez, Michael D. Lovelace, Gayathri Sundaram, Benjamin Heng, Sharron Chow, Christopher Marquis, Gilles J. Guillemin, Bruce Brew, Chennupati Jagadish, Lezanne Ooi, Saman Halgamuge
Формат:	Artigo
Язык:	Inglês
Опубликовано:	Nature Portfolio 2024-06-01
Серии:	Scientific Reports
Online-ссылка:	https://doi.org/10.1038/s41598-024-63511-z
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