Title: Semi-supervised dimensionality reduction based on local estimation error

Authors: Xianfa Cai; Jia Wei; Guihua Wen; Zhiwen Yu; Yongming Cai; Jie Li

Addresses: School of Medical Information Engineering, Guangdong Pharmaceutical University, Guangzhou, 510006, China ' School of Computer Science and Engineering, South China University of Technology, Guangzhou, 510006, China ' School of Computer Science and Engineering, South China University of Technology, Guangzhou, 510006, China ' School of Computer Science and Engineering, South China University of Technology, Guangzhou, 510006, China ' School of Medical Information Engineering, Guangdong Pharmaceutical University, Guangzhou, 510006, China ' School of Medical Information Engineering, Guangdong Pharmaceutical University, Guangzhou, 510006, China

Abstract: The construction of a graph is extremely important in graph-based semi-supervised learning. However, it is unstable by virtue of sensitivity to the selection of neighbourhood parameter and inaccuracy of the edge weights. Inspired by the good performance of the local learning method, this paper proposes a semi-supervised dimensionality reduction based on local estimation error (LEESSDR) algorithm by utilising local learning projections (LLP) to semi-supervised dimensionality reduction. The algorithm sets the edge weights through minimising the local estimation error and can effectively preserve the global geometric structure as well as the local one of the data. Since LLP does not require its input space to be locally linear, even if it is nonlinear, LLP maps it to the feature space by using kernel functions and then obtains its local estimation error in the feature space. The effectiveness of the proposed method is verified on two popular face databases with promising classification accuracy and favourable robustness.

Keywords: local learning projections; LLP; side-information; semi-supervised learning; graph construction.

DOI: 10.1504/IJHPCN.2019.099745

International Journal of High Performance Computing and Networking, 2019 Vol.14 No.1, pp.69 - 76

Received: 11 May 2016
Accepted: 27 Dec 2016
Published online: 21 May 2019 *

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