Fusion Algorithm Based on Improved Linear Local Tangent Space Alignment Algorithm

Abstract

A uncorrelated adaptive discriminant linear local tangent space alignment (UDALLTSA) is proposed based on improved linear local tangent space alignment algorithm. The algorithm uses an adaptive neighborhood selection to select the appropriate neighborhood, and introduces curvature to amend the model, modifies the constraints of the objective function by use inter-class scatter matrix, and constraints on basis vectors to compute the best projection matrix. By comparing the results of the experiments, it shows that after integrating the discriminant information into the algorithm , uncorrelated constraints and adaptive neighborhood selection can well improve the recognition rate and robustness, thus, possessing good noise immunity, and eliminating redundant information of base vectors, make this fusion algorithm a supervised learning algorithm.