Abstract
Locally linear embedding algorithm (LLE) , It makes up the shortcomings that the manifold learning algorithm can be only applied to training samples but not be extended to test samples . However, due to the presence of its Low-dimensional feature space redundant information,and its sample category information does not integrate into a low-dimensional embedding. For this shortcoming, here we introduce the two improved algorithms:the local linear maximum dispersion matrix algorithm (FSLLE) and the adaptive algorithm (ALLE), and the combinations of the above two algorithms.With this experience,combined Garbol and locally linear embedding algorithm (LLE) to compare each conclusion. The results proved to be effective elimination of redundant information among basis vectors and improve the recognition rate.
Sign-in/Register to access full text options 
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 callDisclaimer: 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.
