Abstract: In view of the problem of extrapolating the embedding of a manifold learning from the given data points to incremental data points, the incremental local tangent space alignment algorithm is presented. The method of global coordinates matrix iteration and lower-dimensional coordinates computation is introduced. For incremental learning, the global coordinates of affect points are recomputed, and the updated global coordinates matrix is used to construct global coordinate of the new data points. The lower-dimensional embedding coordinates of the incremental points are formulated by the updated global coordinate matrix and lower-dimensional embedding coordinates of the given data points. The lower-dimensional embedding coordinates of all points are updated by the eigenvalue iteration algorithm. Experiments with the proposed method are carried out to identify the four different mode of the vibration signal of rolling bearing. The experiments result of the incremental points embedding and scatter of the different sorts demonstrates that the incremental local tangent space alignment algorithm can extrapolate the given data points manifold to the incremental learning and the incremental data points can embed effectively to the previously learned manifold.

Key words: Incremental learning, Local tangent space alignment algorithm, Pattern recognition, Rolling bearing