A dimension reduction technique applied to regression on high dimension, low sample size neurophysiological data sets

A dimension reduction technique applied to regression on high dimension, low sample size neurophysiological data sets

Abstract Background A common problem in neurophysiological signal processing is the extraction of meaningful information from high dimension, low sample size data (HDLSS). We present RoLDSIS (regression on low-dimension spanned input space), a regression technique based on dimensionality reduction t...

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Bibliographic Details
Main Authors: Adrielle C. Santana, Adriano V. Barbosa, Hani C. Yehia, Rafael Laboissière
Format: Article
Language:English
Published: BMC 2021-01-01
Series:BMC Neuroscience
Subjects:
Electroencephalography
Event-related potentials
Linear regression
High dimension low sample size problem
Dimension reduction
Phonemic categorization
Online Access:https://doi.org/10.1186/s12868-020-00605-0

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