Abstract

Since 2000, manifold learning methods have been extensively studied, and demonstrated excellent performance in dimensionality reduction in some application scenarios. However, they still have some drawbacks in approximating real nonlinear relationships during the dimensionality reduction process, thus are unable to retain the original data’s structure well. In this paper, we propose an incremental version of the manifold learning algorithm LTSA based on kernel method, which is called IKLSTA, the abbreviation of Incremental Kernel LTSA. IKLTSA exploits the advantages of kernel method and can detect the explicit mapping from the high-dimensional data points to their low-dimensional embedding coordinates. It is also able to reflect the intrinsic structure of the original high dimensional data more exactly and deal with new data points incrementally. Extensive experiments on both synthetic and real-world data sets validate the effectiveness of the proposed method.

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

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.