论文类型：会议论文
收录刊物：CPCI-S、EI、Scopus
卷号：0
页面范围：1213-1218
关键字：Unsupervised feature selection; clustering; triplet; ordinal locality
摘要：Unsupervised feature selection has shown significant potential in distance-based clustering tasks. This paper proposes a novel triplet induced method. Firstly, a triplet-based loss function is introduced to enforce the selected feature groups to preserve ordinal locality of original data, which contributes to distance-based clustering tasks. Secondly, we simplify the orthogonal basis clustering by imposing an orthogonal constraint on the feature projection matrix. Consequently, a general framework for simultaneous feature selection and clustering is discussed. Thirdly, an alternating minimization algorithm is employed to efficiently optimize the proposed model together with rapid convergence. Extensive comparison experiments on several benchmark datasets well validate the encouraging gain in clustering from our proposed method.