Smoothed Multi-View Subspace Clustering
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 ra...
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Zusammenfassung: | 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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DOI: | 10.48550/arxiv.2106.09875 |