关键词: 多标记, 标记关系, 非负矩阵分解（NMF）, l₁/l₂范数, l₁范数

Abstract:  In multi-label learning, the correlation between labels has been more and more widely used, and it is necessary to show the relationship between them. Compared with previous studies, training process is extremely complicated and time-consuming due to the high dimensionality feature of multi-label data, so sparse optimization becomes essential; meanwhile, the relationship among labels has not been thoroughly excavated, so how to learn multi-label classification and study the correlation between all markers more effectively and conveniently becomes a necessity. This paper presents a method constraint on double sparse representation in multi-label classification, which first uses l₁/l₂-norm regularization to sparse multi-label data to convenient for following researches, and then applies l₁-norm into community detection to effectively detect communities. By this it shows the deep meaning of label relationship. Experiments show that this method can rapidly and accurately study and train multiple labels, and accurately display label connection at the same time.

Key words: multi-label, label relation, non-negative matrix factorization (NMF); l₁/l₂-norm; l₁-norm