Sparsity and Nullity: Paradigm for Analysis Dictionary Learning

Sparsity and Nullity: Paradigm for Analysis Dictionary Learning Report Title Sparse models in dictionary learning have been successfully applied in a wide variety of machine learning and computer vision problems, and have also recently emerged with increasing research interest. Another interesting related problem based on linear equality constraint, namely the sparse null space problem (SNS), first appeared in 1986 and has since inspired results on sparse basis pursuit. In this paper, we investigate the relation between the SNS problem and the analysis dictionary learning problem, and show that the SNS problem plays a central role, and may be utilized to solve dictionary learning problems. Moreover, we propose an efficient algorithm of sparse null space basis pursuit, and extend it to a solution of analysis dictionary learning. Experimental results on numerical synthetic data and real-world data are further presented to validate the performance of our method. SPARSITY AND NULLITY: PARAGIDMS FOR ANALYSIS DICTIONARY LEARNING XIAO BIAN, HAMID KRIM , ALEX BRONSTEIN , AND LIYI DAI Abstract. Sparse models in dictionary learning have been successfully applied in a wide variety of machine learning and computer vision problems, and have also recently emerged with increasing research interest. Another interesting related problem based on linear equality constraint, namely the sparse null space problem (SNS), first appeared in 1986 and has since inspired results on sparse basis pursuit. In this paper, we investigate the relation between the SNS problem and the analysis dictionary learning problem, and show that the SNS problem plays a central role, and may be utilized to solve dictionary learning problems. Moreover, we propose an efficient algorithm of sparse null space basis pursuit, and extend it to a solution of analysis dictionary learning. Experimental results on numerical synthetic data and real-world data are further presented to validate the performance of our method. Sparse models in dictionary learning have been successfully applied in a wide variety of machine learning and computer vision problems, and have also recently emerged with increasing research interest. Another interesting related problem based on linear equality constraint, namely the sparse null space problem (SNS), first appeared in 1986 and has since inspired results on sparse basis pursuit. In this paper, we investigate the relation between the SNS problem and the analysis dictionary learning problem, and show that the SNS problem plays a central role, and may be utilized to solve dictionary learning problems. Moreover, we propose an efficient algorithm of sparse null space basis pursuit, and extend it to a solution of analysis dictionary learning. Experimental results on numerical synthetic data and real-world data are further presented to validate the performance of our method.