Learning representations from multiple manifolds
A framework to learn joint embedding space from multiple manifold data is presented. Intra-manifolds' geometric structure and inter-manifolds' structure are preserved. Implicit correspondence between the points across different data sets is estimated. Several embedding examples in different data sets are provided. Current spectral-embedding approaches are extended to handle multiple manifolds.
correspondence manifold learning dimensionality reduction joint manifold representation