Locality preserving KSVD for nonlinear manifold learning

Discovering the intrinsic low-dimensional structure from high-dimensional observation space (e.g., images, videos), in many cases, is critical to successful recognition. However, many existing nonlinear manifold learning (NML) algorithms have quadratic or cubic complexity in the number of data, which makes these algorithms computationally exorbitant in processing real-world large-scale datasets. Randomly selecting a subset of data points is very...