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• W3208596917 abstract "In recent years, multi-view subspace clustering has achieved impressive performance due to the exploitation of complementary information across multiple views. However, multi-view data can be very complicated and are not easy to cluster in real-world applications. Most existing methods operate on raw data and may not obtain the optimal solution. In this work, we propose a novel multi-view clustering method named smoothed multi-view subspace clustering (SMVSC) by employing a novel technique, i.e., graph filtering, to obtain a smooth representation for each view, in which similar data points have similar feature values. Specifically, it retains the graph geometric features through applying a low-pass filter. Consequently, it produces a “clustering-friendly” representation and greatly facilitates the downstream clustering task. Extensive experiments on benchmark datasets validate the superiority of our approach. Analysis shows that graph filtering increases the separability of classes." @default.
• W3208596917 created "2021-11-08" @default.
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• W3208596917 date "2021-01-01" @default.
• W3208596917 modified "2023-10-16" @default.
• W3208596917 title "Smoothed Multi-view Subspace Clustering" @default.
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• W3208596917 doi "https://doi.org/10.1007/978-981-16-5188-5_10" @default.
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