Neighborhood preserving embedding with autoencoder
Ruisheng Ran, Jinping Wang, Bin Fang, Weiming Yang · Digital Signal Processing · 2023
Neighborhood preserving embedding (NPE) is a classical method for dimensionality reduction (DR), and it is a linear version of the locally linear embedding method. However, NPE and all its variants only consider the one-way mapping from high-dimensional space to low-dimensional space. The projected low-dimensional data may not accurately and effectively “represent” the original samples. To address this problem, we improve NPE based on linear autoencoder . The conventional projection of NPE is considered as the encoding stage, and the decoder stage is a reconstruction from the low-dimensional space to the original high-dimensional space, which is the key to maintaining more significant information. Based on this, we propose a new NPE method called NPEAE (neighborhood preserving embedding with autoencoder) in this paper. NPEAE performs excellently in face recognition, handwritten character categorization, object classification, etc. The experiments on MNIST, COIL-20, the Extended Yale B, Olivetti Research Laboratory (ORL), and FERET show that NPEAE has a higher recognition accuracy than other comparative methods.