In soft sensor field

just-in-time learning (JITL) is an effective approach tomodel nonlinear and time varying processes. However

most similarity criterions in JITL are computed in the input space onlywhile ignoring important output information

whichmay lead to inaccurate construction of relevant sample set. To solve this problem

we propose a novel supervised feature extraction method suitable for the regression problemcalled supervised local and non-local structure preserving projections (SLNSPP)

in which both input and output information can be easily and effectively incorporated through a newly defined similarity index. The SLNSPP can not only retain the virtue of locality preserving projections but also prevent faraway points from nearing after projection

which endues SLNSPP with powerful discriminating ability. Such two good properties of SLNSPP are desirable for JITL as they are expected to enhance the accuracy of similar sample selection. Consequently

we present a SLNSPP-JITL framework for developing adaptive soft sensor

including a sparse learning strategy to limit the scale and update the frequency of database. Finally

two case studies are conducted with benchmark datasets to evaluate the performance of the proposed schemes. The results demonstrate the effectiveness of LNSPP and SLNSPP.