Representative Selection with Structured Sparsity
Date of Issue2016
School of Electrical and Electronic Engineering
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.
© 2016 Elsevier. This is the author created version of a work that has been peer reviewed and accepted for publication by Pattern Recognition, Elsevier. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [http://dx.doi.org/10.1016/j.patcog.2016.10.014].