基于字典原子与类标签关系的字典学习

Dictionary Learning Based on Relationship Between Atoms and Labels

在稀疏表示理论中,如何构造字典和更新字典,而能得到一个辨别能力强的字典,始终是一个重要的开放问题,针对这一问题,提出了基于字典原子与类标签关系的字典学习方法.建立一个基于两者关系的矩阵,随着更新字典原子而更新关系矩阵,通过更新关系矩阵来构成字典自适应地确定原子与类标签的关系,提高字典的判别能力,为后续的分类识别提供必要的保证.该方法既避免了共享字典判别能力差的问题,又避免了因单独训练字典而占用大量时间和内存的缺点.在构建字典模型中,引用l21范数约束残差值来去除噪声,使之既能处理稀疏噪声,也能处理非稀疏噪声,提高了字典对噪声的鲁棒性.大量的实验结果证明,该方法较其他的字典学习方法鲁棒性强、识别率高.

In sparse representation theory, how to construct a dictionary and update dictionary making the dictionary discriminative is still an open problem. In order to solve this problem, a dictionary learning method was presented based on relationship between atoms and labels and a matrix was built based on the relationship between them. Then the matrix was updated with the update of the dictionary atoms. The adaptive relationship between atoms and labels of the matrix was constructed, improves the discriminant ability of dictionary, the necessary guarantee for the classification was provided later. This method not only avoided the poor discriminant ability of the share dictionary, but also avoided the individual training dictionary method taking up lots of time and memory faults. And making use of l21 norm constraining residual to remove noise, can not only deal with sparse noise, but also the non-sparse noise, which is robust to the noise. The experiment results show that the proposed method has robustness and high recognition rate compared with other dictionary learning methods.