Multi-manifold LLE learning in pattern recognition
We introduce Multiple Manifold Locally Linear Embedding (MM-LLE) learning. This method learns multiple manifolds corresponding to multiple data classes. MM-LLE uses supervised neighborhood selection in learning multiple manifolds. It finds the optimum low-dimensional space by minimizing the nearness of manifolds. Results show that MM-LLE outperforms many well-known manifold learning algorithms.
multi-manifolds neighborhood selection manifold learning multiple classes near manifolds