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Structural Deep Clustering Network | Deyu Bo
; Xiao Wang
; Chuan Shi
; Meiqi Zhu
; Emiao Lu
; Peng Cui
; | Date: |
5 Feb 2020 | Abstract: | Clustering is a fundamental task in data analysis. Recently, deep clustering,
which derives inspiration primarily from deep learning approaches, achieves
state-of-the-art performance and has attracted considerable attention. Current
deep clustering methods usually boost the clustering results by means of the
powerful representation ability of deep learning, e.g., autoencoder, suggesting
that learning an effective representation for clustering is a crucial
requirement. The strength of deep clustering methods is to extract the useful
representations from the data itself, rather than the structure of data, which
receives scarce attention in representation learning. Motivated by the great
success of Graph Convolutional Network (GCN) in encoding the graph structure,
we propose a Structural Deep Clustering Network (SDCN) to integrate the
structural information into deep clustering. Specifically, we design a delivery
operator to transfer the representations learned by autoencoder to the
corresponding GCN layer, and a dual self-supervised mechanism to unify these
two different deep neural architectures and guide the update of the whole
model. In this way, the multiple structures of data, from low-order to
high-order, are naturally combined with the multiple representations learned by
autoencoder. Furthermore, we theoretically analyze the delivery operator, i.e.,
with the delivery operator, GCN improves the autoencoder-specific
representation as a high-order graph regularization constraint and autoencoder
helps alleviate the over-smoothing problem in GCN. Through comprehensive
experiments, we demonstrate that our propose model can consistently perform
better over the state-of-the-art techniques. | Source: | arXiv, 2002.1633 | Services: | Forum | Review | PDF | Favorites |
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