Manifold Learning based on the Manifold Graph Construction using Sparse Representation

In this paper, a sparse representation based manifold learning method is proposed. The construction of the graph manifold in high dimensional space is the most important step of the manifold learning methods that is divided into local and gobal groups. The proposed graph manifold extracts local and global features, simultanstly. After construction the sparse representation based graph manifold, two linear and nonlinear methods are proposed to extracte the embedded data. The proposed method is compared with the common manifold learning methods, LLE, LEM, LPP and PCA. The results on two Persian handwritten databases, HODA and IFHCDB,show the better performance of the proposed method and the recognition rates of 91.89 and 93.89 are achieved on HODA and IFHCDB, respectively. Also, a modification of the proposed method is proposed to reduce the computational complexity. The results on HODA demonstrate the good performance of the modified method and decrease the computational complexity around 6 times.