Shared component analysis
نویسندگان
چکیده
This paper proposes Shared Component Analysis (SCA) as an alternative to Principal (PCA) for the purpose of dimensionality reduction neuroimaging data. The trend towards larger numbers recording sensors, pixels or voxels leads richer data, with finer spatial resolution, but it also inflates cost storage and computation risk overfitting. PCA can be used select a subset orthogonal components that explain large fraction variance in implicitly equates relevance, data such electroencephalography (EEG) magnetoencephalography (MEG) assumption may inappropriate if (latent) sources interest are weak relative competing sources. SCA instead assumes contribute observable signals on multiple sensors likely interest, case deep within brain result current spread. In SCA, steps normalization applied iteratively, linearly transforming more widely shared across channels appear first component series. explains motivation, defines algorithm, evaluates outcome, sketches wider strategy which this algorithm is example. intended plug-in replacement reduction.
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ژورنال
عنوان ژورنال: NeuroImage
سال: 2021
ISSN: ['2666-9560']
DOI: https://doi.org/10.1016/j.neuroimage.2020.117614