Abstract:
Many multi-view clustering algorithms have been proposed as real-world applications use multi-view data. Existing algorithms usually focus on the cooperation of different visible views in the original space but neglect the influence of hidden information among these visible views, or they only consider hidden information.
Since the available information is not fully exploited, especially the otherness and consistency information in different views, the algorithms are inefficient. Multi-view data’s otherness and consistency information help clustering analyses.
This study proposes MV-Co-VH, a multi-view clustering algorithm with visible and hidden view cooperation. The MV-Co-VH algorithm first projects multiple views from different visible spaces to the common hidden space using non-negative matrix factorization to obtain common hidden view data.
Based on visible and shared hidden views, the clustering procedure uses collaborative learning. Extensive experiments on UCI multi-view datasets and real-world image multi-view datasets show that the proposed algorithm outperforms existing algorithms in clustering.
Note: Please discuss with our team before submitting this abstract to the college. This Abstract or Synopsis varies based on student project requirements.
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