Deep Kernel Principal Component Analysis for multi-level feature learning

Published: 01 Jan 2024, Last Modified: 30 Sept 2024Neural Networks 2024EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: Highlights•A deep kernel principal component analysis (DKPCA) framework is proposed.•Forward and backward couplings between the levels are identified.•Theoretical analysis presents error bounds and higher explained variance than KPCA.•Generative DKPCA is introduced for the pre-image problem in deep kernel methods.•DKPCA is competitive in learning informative multi-level disentangled features.
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