Sparse discriminative multi manifold embedding (SDMME) algorithm was used for feature extraction, graph construction and projection learning were independent, the quality of the graph directly affects the effect of projection learning. In order to solve the problem, a new algorithm named sparse discriminative multi manifold embedding based on graph optimization (GOSDMME) was proposed in this paper. First, in proposed approach, the image matrix was divided into blocks. The matrix blocks on the same image were located on the same manifold. Then, the sparse graph was used to establish the connection relationship between different blocks. Finally, in the framework of the same objective function, the sparse constraint graphs and projections were studied simultaneously. The graphs and projections were learned at the same time, iterate and update the graph and projection to obtain a projection matrix that satisfies the accuracy requirements. The face recognition experiments conducted on Extended Yale B and CMU PIE datasets show that the new algorithm has better recognition performance than the SDMME algorithm.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.