Stacked Network to Realize Spectral Clustering With Adaptive Graph Learning

Junyu Li, Fei Qi, Haoliang Yuan, Cheng Zhong, Hongmin Cai

Abstract: Spectral clustering with graph learning usually performs eigen-decomposition on the adaptive graph to obtain embedded representation for clustering. In terms of adaptive graph learning, the embedded representation is usually treated as the principal component of the graph to help improve graph structure. However, most adaptive graph learning methods only use a single graph layer. Therefore, the extraction power of embedded representation is restricted to single graph layer and insufficient to explore the intrinsic information. To break through this limitation, this article proposes a stacked network to realize spectral clustering with adaptive graph learning (SCnet-AGL). Specifically, the network allows the development of latent embedded representation underlying the multiple graph layers to reveal the intrinsic information. Meanwhile, we have designed an adaptive graph learning scheme to exploit the latent embedded representation for graph learning. With the advantage of the network, an augmented graph is obtained by incorporating the representation information for graph learning layer by layer. Finally, an efficient algorithm with feedback training scheme is proposed for network training. Experiments on real datasets demonstrate the effectiveness of the proposed network, and show that it is feasible to develop latent embedded representation to improve clustering performance.