Contrastive Consistency and Attentive Complementarity for Deep Multi-View Subspace Clustering  

作　　者：Jiao Wang Bin Wu Hongying Zhang 

机构地区：[1]School of Information Engineering,Southwest University of Science and Technology,Mianyang,621010,China

出　　处：《Computers, Materials & Continua》2024年第4期143-160,共18页计算机、材料和连续体（英文）

摘　　要：Deep multi-view subspace clustering (DMVSC) based on self-expression has attracted increasing attention dueto its outstanding performance and nonlinear application. However, most existing methods neglect that viewprivatemeaningless information or noise may interfere with the learning of self-expression, which may lead to thedegeneration of clustering performance. In this paper, we propose a novel framework of Contrastive Consistencyand Attentive Complementarity (CCAC) for DMVsSC. CCAC aligns all the self-expressions of multiple viewsand fuses them based on their discrimination, so that it can effectively explore consistent and complementaryinformation for achieving precise clustering. Specifically, the view-specific self-expression is learned by a selfexpressionlayer embedded into the auto-encoder network for each view. To guarantee consistency across views andreduce the effect of view-private information or noise, we align all the view-specific self-expressions by contrastivelearning. The aligned self-expressions are assigned adaptive weights by channel attention mechanism according totheir discrimination. Then they are fused by convolution kernel to obtain consensus self-expression withmaximumcomplementarity ofmultiple views. Extensive experimental results on four benchmark datasets and one large-scaledataset of the CCAC method outperformother state-of-the-artmethods, demonstrating its clustering effectiveness.

关 键 词：Deep multi-view subspace clustering contrastive learning adaptive fusion self-expression learning 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]