An improved Local Tangent Space Alignment Algorithm Based on Max Linear Patch Partition and its application in multi-pose ear recognition

As an effective nonlinear dimension reduction tool, Local Tangent Space Alignment algorithm (LTSA) can construct the global low-dimensional embedded coordinates of the data points sampled in high-dimensional space. However, there have been some technical problems unresolved as yet for its further applications. In order to solve these problems, an improved Local Tangent Space Algorithm based on Max Linear Patch Partition (MLPPLTSA) is proposed in this paper. First, the sample space is divided into several overlapping patches using a max linear patch partition strategy. Then local principal component analysis is performed on each patch to get the local low-dimensional coordinates of the data points in it. At last the overlapping points in the patches are used to get the global low-dimensional embedded manifold by local affine transformations. The MLPPLTSA can avoid large-scale matrix eigenvalue decomposition when samples are in large quantities and provide more simple transformations for new coming data to calculate the global embedded coordinates. This improved LTSA is applied to the multi-pose ear recognition in this paper. Experimental result shows its validity in image recognition tasks.