关键词: 属性选择, 低秩约束, 图拉普拉斯, 子空间学习, 稀疏正则化

Abstract: Aiming at the problem that unsupervised attribute selection algorithm uses a single method without considering the intrinsic correlation and noise between the data, a low rank unsupervised attribute selection algorithm based on attribute self expression is proposed. Firstly, the sparse regularization is introduced into the attribute self expression loss function to realize unsupervised sparse learning, secondly, the low rank constraint is added to the algorithm coefficient matrix to reduce the influence of noise and outliers, then, the low rank structure and the Tula Laplace regularization are used in the algorithm to make the subspace study both the global and local structures of the data, finally, the algorithm achieves unsupervised learning through attribute self - expression. The experimental results show that the algorithm can converge quickly and achieve global optimum. Compared with SOGFS, PCA, LPP, RSR and other four kinds of algorithm, the classification accuracy rate respectively increases by 16.11%, 14.03%, 9.92% and 4.2%, and the average value of mutual information in each data set is the highest, indicating that the algorithm is effective and efficient.

Key words: attribute selection, low rank constraints, Tula Laplace, subspace learning, sparse regularization