Abstract
In many clustering problems, there are dozens of data which are represented by multiple views. Different views describe different aspects of the same set of instances and provide complementary information. Considering blindly combining the information from different views will degrade the multi-view clustering result, this paper proposes a novel view-weighted multi-view k-means method. Meanwhile, to reduce the adverse effect of outliers, \(l_{2,1}\) norm is employed to calculate the distance between data points and cluster centroids. An alternative iterative update schema is developed to find the optimal value. Comparative experiments on real world datasets reveal that the proposed method has better performance.
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