Block sparse dictionary learning based on recursive least squares
Based on the over-complete dictionary, the signal can be described as sparse linear combination of atoms.Traditionally,the dictionary learning methods are mostly based on a single atom unit.In our framework,sparse subspace clustering is used to categorize the atoms that have the same sparse expressions into groups to form block structure of the dictionary,and then encode the training signal with the sparse coding algorithm,finally applying the recursive least squares method to update the dictionary.Experiments show that our method converges faster with the same iterations,and the signal reconstruction error rate is better than the traditional methods.
block sparse recursive least squares dictionary learning signal sparse representation
Ji Yinghui Ni Yining Peng Hongjing
School of Computer Science and Technology Nanjing Tech University
国际会议
秦皇岛
英文
416-421
2015-09-18(万方平台首次上网日期,不代表论文的发表时间)