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Sparse Centroid-Encoder: A Nonlinear Model for Feature Selection
[article]
2022
arXiv
pre-print
Autoencoders have been widely used as a nonlinear tool for data dimensionality reduction. While autoencoders don't utilize the label information, Centroid-Encoders (CE) use the class label in their learning process. In this study, we propose a sparse optimization using the Centroid-Encoder architecture to determine a minimal set of features that discriminate between two or more classes. The resulting algorithm, Sparse Centroid-Encoder (SCE), extracts discriminatory features in groups using a
arXiv:2201.12910v2
fatcat:sz5oa2qrnfhe7kpgeqddwbyrwu