Datasets in Multi View Clustering with Adaptive Similarity Graph Joint Optimization

Compared to single view learning, multi view learning can often obtain more comprehensive information about the learning object. Therefore, in the field of unsupervised learning, multi view clustering has received great attention from researchers. Among them, graph based multi view clustering has made great research progress in recent years. Graph based multi view clustering generally first learns similar graphs from the original data of each view, and then fuses similar graphs between views to obtain the final clustering result. Therefore, the effectiveness of multi view clustering is determined by the quality of similar graphs and the fusion method of similar graphs. However, existing graph based multi view clustering methods almost focus on the fusion method of similar graphs between views, and lack attention to the quality of similar graphs themselves. Most of these methods are... Learning similar graphs from the original data of each view in isolation and keeping them unchanged during the subsequent graph fusion process. This inevitably results in similar graphs containing noise and redundant information, which in turn affects subsequent graph fusion and clustering. However, a small amount of research that considers the quality of similar graphs, either the construction of similar graphs and the process of graph fusion are directly iterative, Either rank constraints are used in advance during the predefined similarity graph process to further initialize, or some underlying structures of the similarity graph are used to obtain the fusion graph. These methods have little improvement on the similarity graph itself, and the final clustering performance improvement is also very limited. At the same time, existing graph based multi view clustering processes also lack comprehensive consideration of consistency and inconsistency between views, which will seriously affect the final multi view clustering performance. In order to avoid the adverse effects of low-quality predefined similarity graphs on clustering results and comprehensively consider consistency and inconsistency between views to improve the final clustering effect, this paper proposes an adaptive multi view clustering method for joint optimization of similarity graphs. Firstly, the Hadamard product is used to obtain high-quality consistency between views. Partial information is then compared with each predefined similar graph to reconstruct the preset similar graphs for each view. This process strengthens the consistency between views and weakens the inconsistency. Secondly, a joint iterative optimization framework for similar graph reconstruction and graph fusion is designed to achieve adaptive improvement of similar graphs, The ultimate goal is to achieve the combined improvement of similar graphs and clustering results. This method combines the process of improving similar graphs with the process of graph fusion for adaptive iterative optimization, and continuously strengthens the consistency between views and weakens the inconsistency between views during iterative optimization. In addition, this method also integrates some advantages of existing multi view clustering methods, such as self weighting and no additional clustering steps. The effectiveness and superiority of this method have been verified through experiments on nine benchmark datasets and eight comparison methods