Manifold-Preserving Common Subspace Factorization for Feature Matching

Abstract

A method, called Manifold-preserving Common Subspace Factorization, is presented which can be used for feature matching. Motivated by the Graph Regularized Non-negative Matrix Factorization (GNMF) algorithm (Deng et al. 2011), we developed GNMF algorithm by considering a joint factorization of the two feature matrices, which share a common basis matrix. An iterative multiplicative updating algorithm is proposed to optimize the objective, and its convergence is guaranteed theoretically. Our feature matching algorithm operates on the new representations in the common subspace generated by basis vectors. Experiments are conducted on the synthetic and real-world data. The results show that the Manifold-preserving common subspace factorization algorithm provides better matching rates than other matrix-factorization techniques.