Representative Selection with Structured Sparsity. (March 2017) - Wang, Hongxing; Kawahara, Yoshinobu; Weng, Chaoqun; Yuan, Junsong
Abstract: We propose a novel formulation to find representatives in data samples via learning with structured sparsity. To find representatives with both diversity and representativeness, we formulate the problem as a structurally-regularized learning where the objective function consists of a reconstruction error and three structured regularizers: (1) group sparsity regularizer, (2) diversity regularizer, and (3) locality-sensitivity regularizer. For the optimization of the objective, we propose an accelerated proximal gradient algorithm, combined with the proximal-Dykstra method and the calculation of parametric maximum flows. Experiments on image and video data validate the effectiveness of our method in finding exemplars with diversity and representativeness and demonstrate its robustness to outliers. Abstract : Highlights: We propose a novel formulation to find representatives in data samples via learning with structured sparsity. The proposed structured sparsity learning encourages sparsity, diversity and locality-sensitivity in representative selection. An accelerated proximal gradient algorithm is given to optimize the proposed formulation.