Abstract: Hessian LLE algorithm is a classical manifold learning algorithm. However, Hessian LLE is a batch mode. If only new samples are observed, the whole algorithm must run repeatedly and all the former computational results are discarded. So,incremental Hessian LLE(LIHLLE) algorithm was proposed, which preserves local neighborhood relationship between the original space and the embedding space. New sample points were linearly reconstructed with existing embedding results of local neighborhood samples. The proposed method can learn manifold in an incremental way. Simulation results in Swiss roll with hole and frey_rawface database testify the efficiency and accuracy of the proposed algorithms.

Key words: Manifold learning, Hessian LLE, Incremental learning