Incomplete multi-view clustering with cosine similarity

作者：

Highlights：

• We propose incomplete multi-view clustering with cosine similarity (IMCCS) for partitioning incomplete multi-view data.

• IMCCS calculates the cosine similarity of incomplete multi-view data in the original multi-view space.

• Gradient descent with the multiplicative update rule is presented to solve the objective of IMCCS.

• IMCCS outperforms state-of-the-art incomplete multi-view clustering methods.

摘要

•We propose incomplete multi-view clustering with cosine similarity (IMCCS) for partitioning incomplete multi-view data.•IMCCS calculates the cosine similarity of incomplete multi-view data in the original multi-view space.•Gradient descent with the multiplicative update rule is presented to solve the objective of IMCCS.•IMCCS outperforms state-of-the-art incomplete multi-view clustering methods.

论文关键词：Multi-view learning,Missing view,Cosine similarity,Gradient descent,Matrix factorization

论文评审过程：Received 27 November 2020, Revised 18 July 2021, Accepted 14 October 2021, Available online 16 October 2021, Version of Record 23 October 2021.

论文官网地址：https://doi.org/10.1016/j.patcog.2021.108371