自适应不完备多视角聚类  

Adaptive incomplete multi-view clustering

作　　者：陈梅[1] 马学艳 张弛 张锦宏 钱罗雄 CHEN Mei;MA Xueyan;ZHANG Chi;ZHANG Jinhong;QIAN Luoxiong(School of Electronic and Information Engineering,Lanzhou Jiaotong University,Lanzhou 730070,China)

机构地区：[1]兰州交通大学电子与信息工程学院,兰州730070

出　　处：《北京航空航天大学学报》2025年第4期1059-1073,共15页Journal of Beijing University of Aeronautics and Astronautics

基　　金：国家自然科学基金(62266029);甘肃省重点研发计划(21YF5GA053);甘肃省高等学校产业支撑计划(2022CYZC-36)。

摘　　要：高质量的完备初始图能够有效提高不完备多视角聚类的性能,缺失值填充不恰当会导致初始图丢失数据潜在的结构,同时,各视角的仿射图融合不完全会造成学习到的一致表征缺失视角间的互补信息。为此,提出自适应不完备多视角聚类(AIM)模型。在初始图构建中,AIM模型采用有效视角的相似度均值来填充对应位置的缺失值,以获取数据更加完整的潜在结构,同时引入稀疏约束来提高模型对噪声的鲁棒性;在图优化过程中,引入低秩约束捕获数据的全局结构,通过谱约束增强类内数据间的紧密性,使仿射图具有更清晰的块对角结构,并引入一致性约束最小化各视角的仿射图与一致表征之间的差异来捕获视角间的互补信息,得到具有高鉴别特征的一致鲁棒表征图。与9种不完备多视角聚类方法在真实和多种缺失率下仿真的不完备多视角数据集中进行实验对比,结果表明:AIM模型均获得了最好的聚类性能。A high-quality complete initial graph can effectively improve the performance of incomplete multiview clustering.However,inappropriate filling of missing values will lead to the initial graph losing the underlying structure of the data,and incomplete fusion of affine graphs of each view will make the unified learned representations miss the complementary information among the views.To address the aforementioned problems,an adaptive incomplete multi-view clustering(AIM)method was proposed in this paper.In the initial graph construction,AIM used the average value of similarity of valid views to fill the missing values at corresponding positions to obtain a complete potential structure of the data and introduced sparsity constraints to improve the robustness of the model to noise.In the graph optimization process,initially,low-rank constraints were introduced to capture the global structure of the data,followed by spectral constraints to enhance the closeness between data within classes to make the affine graph have a clearer block diagonal structure.The consistency constraints were introduced to minimize the differences between the affine graph and the unified representation of each view to capture the complementary information between the views.Ultimately,a unified robust representation graph with high discriminative features was obtained.The experimental comparisons with nine kinds of incomplete multi-view clustering in real and incomplete multi-view datasets simulated under multiple missing rates demonstrate that AIM obtains the best clustering performance.

关 键 词：自适应构图 低秩表示 图融合 图算法 不完备多视角聚类 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]