Incremental sample dimensionality reduction and recognition based on clustering adaptively manifold learning

To solve the problem that incremental learning of locally linear embedding (LLE) cannot get reconfiguration neighborhood adaptively and powerlessly, a target recognition method of clustering adaptively incremental LLE (C-LLE) is proposed. Firstly, the clustering model of the clustering locally linear structure of high-dimensional data is build, so it is able to solve the problem of neighborhood adaptive reconfiguration. Then the proposed algorithm extracts an explicit dimensionality reduction matrix, and the problem of powerlessly incremental object recognition is solved. Experimental results show that the proposed algorithm is able to extract the low-dimensional manifold structure of high-dimensional data accurately. It also has low incremental dimension reduction error and great target recognition performance.