Robust feature selection method via joint low-rank reconstruction and projection reconstruction - Shuangyan YI; Yongsheng LIANG; Jingjing LU; Wei LIU; Tao HU; Zhenyu HE (Tongxin xuebao)
Aiming at the problem that current feature selection methods were still affected by noise and cannot effectively unify clustering and reconstruction effects, a robust feature selection method was proposed.A robust reconstruction error term was built by making the difference between low-rank reconstruction and projection reconstruction.After that, the features for clustering were selected from the reconstructed data instead of the original data.The learning of clean data and feature selection technique are allowed for joint learning and promote each other, thereby improving the robustness of the method on noisy data, and effectively unifying reconstruction and clustering.Compared with several kinds of graph embedding feature selection and reconstruction feature selection methods on five datasets, the experimental results showed that, except for the LUNG noise dataset, the proposed method outperforms the comparative feature selection method under both evaluation indicators (ACC and NMI).