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.
@article{arxiv.2106.09875,
title = {Smoothed Multi-View Subspace Clustering},
author = {Peng Chen and Liang Liu and Zhengrui Ma and Zhao Kang},
journal= {arXiv preprint arXiv:2106.09875},
year = {2021}
}
Comments
Accepted by International Conference on Neural Computing for Advanced Applications 2021