Output-related feature representation for soft sensing based on supervised locality preserving projections

摘要

Locality preserving projections (LPP) is a useful tool for learning the manifold of high dimensional data, which is a linear approximation of nonlinear Laplacian Eigenmap (LE). However, the original LPP algorithm is an unsupervised method that extracts features without any reference to the output information. In this paper, a supervised LPP (SLPP) framework is proposed for output-related feature extraction in soft sensor applications. In the SLPP framework, the output information is utilized to guide the procedures for constructing the adjacent graph and calculating the weight matrix, with which the intrinsic structure of the data can be better described. Two specific SLPP algorithms are described. For performance evaluation of the proposed methods, experiments on a numerical example and an industrial iromaking process are carried out. The results show the effectiveness of the proposed framework.