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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| 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 |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-024-77080-8 |
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