基于稀疏表示的多标记学习算法

A Multi-Label Learning Algorithm Based on Sparse Representation

为解决多标记数据的分类问题,提出基于稀疏表示的多标记学习算法.首先将待分类样本表示为训练样本集上的稀疏线性组合,基于l1-最小化方法求得最稀疏的系数解.然后利用稀疏系数的判别信息提出一个计算待分类样本对各标记的隶属度的方法.最后根据隶属度对标记进行排序,进而完成分类.在Yeast基因功能分析、自然场景分类和web页面分类上的实验表明,该算法能够有效解决多标记数据的分类问题,与其它方法相比取得更好的结果.

To solve the problem of multi-label data classification,a multi-label learning algorithm based on sparse representation is proposed.The testing samples are treated as a sparse linear combination of training samples,and the sparsest coefficients are obtained by using l1-minimization.Then,the discriminating information of sparse coefficients is utilized to calculate membership function of the testing sample.Finally,the labels are ranked according to the membership function and the classification is completed.Extensive experiments are conducted on gene functional analysis,natural scene classification and web page categorization,and experimental results demonstrate the effectiveness of the proposed method.The results also show that the proposed method based on sparse representation achieves better results than other algorithms.