Locality Preserving Projections with Autoencoder

Abstract

Locality Preserving Projections (LPP) is a popular dimensionality reduction method in the manifold learning field. However, LPP and all its variants only consider the one-way mapping from the high-dimensional space to the low-dimensional space and have no reverse verification, resulting in inaccurate low-dimensional embeddings. In this paper, we propose a new LPP method, called LPPAE (Locality Preserving Projections with Autoencoder), based on the linear Autoencoder. It constructs a two-way mapping: at the encoding stage, the conventional projection of LPP is viewed as a mapping from the high-dimensional space to the low-dimensional space. At the decoding stage, the low-dimensional embeddings are mapped back to the original high-dimensional space. The main contributions of the new method are: (1) This design not only preserves the neighborhood relationship of the data but more importantly, ensures that the low-dimensional embeddings can more accurately ”represent” the original data, thus significantly improving the performance of LPP. Experimental results on Handwritten Alphadigits, COIL-20, Yale, AR datasets show that the recognition rates of LPPAE are 26.06, 10.09, 5.40, and 8.86% higher than those of the original LPP respectively. On the MNIST dataset, compared to some of the latest improvements of LPP, including LPPMDC, LAPP, LPP+TR, and DNLPP, the recognition rate of LPPAE has been improved by 12.50, 38.10, 9.10, and 2.61%, respectively. (2) LPPAE regards the conventional LPP as an encoder, which is a new perspective. The idea of LPPAE can be used as a general framework and then extended to other manifold learning methods, and then a series of new methods can be developed.