Intra-class multi-output regression based subspace analysis

A common challenge when dealing with heterogenous tasks such as face expression analysis, face and object recognition is high dimensionality and extreme appearance variations within each class. To handle such scenarios, we formulate a supervised Non-negative Matrix Factorization (NMF) based subspace learning technique that simultaneously preserves the intra-class regression information (local) and enhances inter-class discrimination (global) in the low dimensional embedding. Our method leverages the multi-dimensional image labels that quantify the within class regression to learn the subspaces for recognition. In addition, our formulation includes a novel multi-output regression based NMF algorithm.