Embedding new observations via sparse-coding for non-linear manifold learning
A framework based on sparse coding is proposed for out-of-sample embedding. The locality preserving property is jointly used with sparse coding. Classification performance and embedding consistency with batch modes are assessed. Experiments are conducted on six public face databases. K-nearest neighbor and Kernel Support Vector Machines classifiers are used.
non-linear manifold learning out-of-sample embedding sparse representation face recognition