GRAPH-BASED FEATURES EXTRACTION VIA DATUM ADAPTIVE WEIGHTED COLLABORATIVE REPRESENTATION FOR FACE RECOGNITION
Waqas Jadoon, Yi Zhang, Lei Zhang · International Journal of Pattern Recognition and Artificial Intelligence · 2014
We propose a novel unsupervised subspace learning method to optimize graph construction for face recognition called Datum Adaptive Weighted Collaborative Representation (DAWCR). Different from sparsity preserving projection (SPP), a recently proposed linear dimensionality reduction method inspired by sparse representation, where graph is constructed using sparse reconstructive relationship by minimizing a l1-regularization-based objective function, DAWCR aims to optimize the graph construction by incorporating the locality structure and features variance among data elements into a unified framework using regularized linear representation i.e. weighted regularized least square using l2-minimization approach. The neighborhood selection method in DAWCR is datum dependent, moreover neighborhood size for each datum is chosen automatically by considering data distribution probability. Hence the resulting graph is sparse, models the nonlinear geometry of data set, and conveys more discriminate information. The DAWCR problem formulation has the algebraic solution without involving any parameter tuning for optimal neighbors selection, which makes it computationally efficient than SPP. Extensive experiments on several publicly available real-world face and some UCI data sets, are conducted to verify the feasibility and effectiveness of the proposed method. Experimental results show that the proposed method achieves competitive performance with encouraging results.