基于局部学习的半监督多标记分类算法

Semi-supervised multi-label classification algorithm based on local learning

针对在求解半监督多标记分类问题时通常将其分解成若干个单标记半监督二类分类问题从而导致忽视类别之间内在联系的问题,提出基于局部学习的半监督多标记分类方法。该方法避开了多个单标记半监督二类分类问题的求解,采用"整体法"的研究思路,利用基于图的方法,引入基于样本的局部学习正则项和基于类别的拉普拉斯正则项,构建了问题的正则化框架。实验结果表明,所提算法具有较高的查全率和查准率。

Semi-supervised multi-label classification problem is usually decomposed into a set of single-label semi-supervised binary classification problems.However,it results in the ignorance of the inner relationship between labels.A semi-supervised multi-label classification algorithm was presented,which avoided multiple single-label semi-supervised binary classification problems but adopted the overall approach in this paper.On the basis of undirected graph,local learning regularizer for data points and Laplace regularizer for labels were introduced and regularization framework of the problem was constructed.The experimental result shows the proposed algorithm has higher precision and recall.