1. This paper presents a new graph learning-based multi-view clustering approach, which simultaneously and explicitly models the multi-view consistency and inconsistency in a unified optimization model.
2. The proposed method is applicable to both similarity graphs and dissimilarity graphs, leading to two graph fusion-based variants, namely, distance (dissimilarity) graph fusion and similarity graph fusion.
3. Experiments on various multi-view datasets demonstrate the superiority of the proposed approach.
The article “ICDM’19.pdf” presents a new graph learning-based multi-view clustering approach for improving the performance of multi-view clustering tasks. The article is well written and provides an overview of the proposed method as well as its advantages over existing methods. The authors provide evidence for their claims by presenting experiments on various multi-view datasets that demonstrate the superiority of their approach.
The article does not appear to be biased or one-sided in its reporting, as it provides an objective overview of the proposed method without making any unsupported claims or omitting any points of consideration. Furthermore, all claims made are supported by evidence from experiments conducted on various datasets, thus providing sufficient evidence for the reliability of the results presented in this article.
In terms of trustworthiness and reliability, this article appears to be trustworthy and reliable due to its clear presentation of information and lack of promotional content or partiality towards any particular view or opinion. Additionally, possible risks associated with using this method are noted throughout the article, thus ensuring that readers are aware of potential issues that may arise when using this method in practice.