A tied-weight autoencoder for the linear dimensionality reduction of sample data

Abstract Dimensionality reduction is a method used in machine learning and data science to reduce the dimensions in a dataset. While linear methods are generally less effective at dimensionality reduction than nonlinear methods, they can provide a linear relationship between the original data and th...

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Bibliographic Details
Main Authors: Sunhee Kim, Sang-Ho Chu, Yong-Jin Park, Chang-Yong Lee
Format: Article
Language:English
Published: Nature Portfolio 2024-11-01
Series:Scientific Reports
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Online Access:https://doi.org/10.1038/s41598-024-77080-8
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