Abstract
Recently, locality sensitive discriminant analysis (LSDA) was proposed for dimensionality reduction. As far as matrix data, such as images, they are often vectorized for LSDA algorithm to find the intrinsic manifold structure. Such a matrix-to-vector transform may cause the loss of some structural information residing in original 2D images. Firstly, this paper proposes an algorithm named two-dimensional locality sensitive discriminant analysis (2DLSDA), which directly extracts the proper features from image matrices based on LSDA algorithm. And the experimental results on the ORL database show the effectiveness of the proposed algorithm. After that, 2DLSDA plus Fisherface, which was presented for the further dimensionality reduction, was compared with other dimention reduction methods, namely Eigenface, LSDA and 2DLSDA plus PCA. Experiments show that conducting Fisherface after 2DLSDA achieves high recognition accuracy.
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.
