With the rapid development of the Internet, there are a large number of high-dimensional multi-label data to be processed in real life. To save resources and time, semi-supervised multi-label feature selection, as a dimension reduction method, has been widely used in many machine learning and data mining. In this paper, we design a new semi-supervised multi-label feature selection algorithm. First, we construct an initial similarity matrix with supervised information by considering the similarity between labels, so as to learn a more ideal similarity matrix, which can better guide feature selection. By combining latent representation with semi-supervised information, a more ideal pseudo-label matrix is learned. Second, the local manifold structure of the original data space is preserved by the manifold regularization term based on the graph. Finally, an effective alternating iterative updating algorithm is applied to optimize the proposed model, and the experimental results on several datasets prove the effectiveness of the approach.

随着互联网的快速发展，现实生活中有大量的高维多标签数据需要处理。为了节省资源和时间，半监督多标签特征选择作为一种降维方法，已广泛应用于许多机器学习和数据挖掘中。在本文中，我们设计了一种新的半监督多标签特征选择算法。首先，我们考虑标签之间的相似性，构建带有监督信息的初始相似度矩阵，从而学习到更理想的相似度矩阵，从而更好地指导特征选择。通过将潜在表示与半监督信息相结合，学习到更理想的伪标签矩阵。其次，基于图的流形正则化项保留了原始数据空间的局部流形结构。最后，应用有效的交替迭代更新算法来优化所提出的模型，并且在多个数据集上的实验结果证明了该方法的有效性。