A novel multi-view clustering approach via proximity-based factorization targeting structural maintenance and sparsity challenges for text and image categorization
作者：
Highlights：
• We propose a proximity-based factorization model for multi-view clustering.
• The proposed model is robust to sparse data.
• The algorithm constructs proximity matrices for each view.
• These matrices are used to model distribution of data points in the common subspace.
• The performance of our algorithm is shown both analytically and experimentally.
摘要
•We propose a proximity-based factorization model for multi-view clustering.•The proposed model is robust to sparse data.•The algorithm constructs proximity matrices for each view.•These matrices are used to model distribution of data points in the common subspace.•The performance of our algorithm is shown both analytically and experimentally.
论文关键词：Multi-view learning,Clustering,High-order proximity,Spectral clustering,Non-negative Matrix Factorization
论文评审过程：Received 29 September 2020, Revised 2 January 2021, Accepted 12 February 2021, Available online 13 March 2021, Version of Record 13 March 2021.
论文官网地址：https://doi.org/10.1016/j.ipm.2021.102546
