会议专题

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

2015 Fifth International Conference on Instrumentation and Measurement,Computer,Communication and Control (IMCCC2015)(第五届仪器测量、计算机通信与控制国际会议)

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)