| Summary: | The rapid advancement in hardware technology has generated a substantial volume
of multi-view data with diverse representation formats. However, in practical applications,
the collected multi-view data is often affected by noise due to various factors
in the natural environment, making it challenging to obtain a high-quality dataset. To
address the noise problem in multi-view data, this study enhances the gbs method and
develops a new self-weighted graph multi-view clustering algorithm (swmcan). Particularly,
swmcan addresses multi-view data noise using the l1-norm and optimizes
the objective function through a novel iterative reweighted method. Extensive experiments
on synthetic and real-world datasets consistently demonstrate that the swmcan
algorithm outperforms recently proposed multi-viewclustering methods regarding clustering
performance and noise robustness. Although the swmcan algorithm solves
the noise problem in multi-view data, its initial and final graphs are independent and
cannot learn from each other. To address this issue, this study incorporated joint graph
learning from the gmc algorithm into swmcan, creating a new algorithm called
swmcan-jg. The swmcan-jg algorithm effectively tackles both noise and independence
problems simultaneously.
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