Adaptive Sample-Level Graph Combination for Partial Multiview Clustering

Liu Yang, Chenyang Shen, Qinghua Hu, Liping Jing, Yingbo Li

Research output: Contribution to journalArticlepeer-review

16 Scopus citations


Multiview clustering explores complementary information among distinct views to enhance clustering performance under the assumption that all samples have complete information in all available views. However, this assumption does not hold in many real applications, where the information of some samples in one or more views may be missing, leading to partial multiview clustering problems. In this case, significant performance degeneration is usually observed. A collection of partial multiview clustering algorithms has been proposed to address this issue and most treat all different views equally during clustering. In fact, because different views provide features collected from different angles/feature spaces, they might play different roles in the clustering process. With the diversity of different views considered, in this study, a novel adaptive method is proposed for partial multiview clustering by automatically adjusting the contributions of different views. The samples are divided into complete and incomplete sets, while a joint learning mechanism is established to facilitate the connection between them and thereby improve clustering performance. More specifically, the method is characterized by a joint optimization model comprising two terms. The first term mines the underlying cluster structure from both complete and incomplete samples by adaptively updating their importance in all available views. The second term is designed to group all data with the aid of the cluster structure modeled in the first term. These two terms seamlessly integrate the complementary information among multiple views and enhance the performance of partial multiview clustering. Experimental results on real-world datasets illustrate the effectiveness and efficiency of our proposed method.

Original languageEnglish (US)
Article number8902218
Pages (from-to)2780-2794
Number of pages15
JournalIEEE Transactions on Image Processing
StatePublished - 2020
Externally publishedYes


  • Partial multiview clustering
  • adaptive weights
  • graph combination

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design


Dive into the research topics of 'Adaptive Sample-Level Graph Combination for Partial Multiview Clustering'. Together they form a unique fingerprint.

Cite this