Abstract
Laplacian embedding (LE) has been widely used to learn the intrinsic structure of data. However, LE ignores the diversity and may impair the local topology of data, resulting in unstable and inexact intrinsic structure representation. In this article, we build an objective function to learn the intrinsic structure that well characterizes both the similarity and diversity of data, and then incorporate this structure representation into linear discriminant analysis to build a semi-supervised approach, called stable semi-supervised discriminant learning (SSDL). Experimental results on two databases demonstrate the effectiveness of our approach.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.