Smoothed Multi-View Subspace Clustering

• URL: http://arxiv.org/abs/2106.09875v1
• Date: Fri, 18 Jun 2021 02:24:19 GMT
• Title: Smoothed Multi-View Subspace Clustering
• Authors: Peng Chen, Liang Liu, Zhengrui Ma, Zhao Kang
• Abstract summary: We propose a novel multi-view clustering method named smoothed multi-view subspace clustering (SMVSC) It employs a novel technique, i.e., graph filtering, to obtain a smooth representation for each view. Experiments on benchmark datasets validate the superiority of our approach.
• Score: 14.77544837600836
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In recent years, multi-view subspace clustering has achieved impressive performance due to the exploitation of complementary imformation 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.

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