Simple yet Effective Incomplete Multi-view Clustering: Similarity-level Imputation and Intra-view Hybrid-group Prototype Construction
Shengju Yu Zhibin Dong Siwei Wang Pei Zhang Yi Zhang Xinwang Liu Naiyang Guan Tiejun Li Yiu-ming Cheung
Most of incomplete multi-view clustering (IMVC) methods typically choose to ignore the missing samples and only utilize observed unpaired samples to construct  bipartite similarity. Moreover, they employ a single quantity of prototypes to extract the information of $\textbf{all}$ views.   To eliminate these drawbacks, we present a simple yet effective IMVC approach, SIIHPC, in this work.  It firstly transforms partial bipartition learning into original sample form by virtue of   reconstruction concept to split out of  observed similarity, and then loosens traditional non-negative constraints via regularizing samples to more freely characterize the similarity.  Subsequently, 
it learns to recover the incomplete parts by utilizing the connection built between the similarity exclusive on respective view and the consensus graph shared for all views.  On this foundation,  it further introduces a group of hybrid prototype quantities for each individual view  to flexibly extract the data features   belonging to each view itself.  Accordingly, the resulting graphs are with various scales and  describe the overall similarity more comprehensively. It is worth mentioning that these all are optimized in one unified learning framework, 
which makes it possible for them to  reciprocally promote.  Then, to effectively solve the formulated optimization problem,  we design an ingenious auxiliary function that is with theoretically proven monotonic-increasing properties.  Finally, the clustering results are obtained by implementing spectral grouping action on the eigenvectors  of stacked multi-scale consensus similarity.  Experimental results confirm the  effectiveness of  SIIHPC.
incomplete multi-view clustering, mulit-view clustering, clustering