Deep spectral clustering network for incomplete multi-view clustering

Spectral clustering is a helpful technique for clustering non-convex data, which extends the clustering to data with multiple partial views. Still, it has a higher running time thanks to cubic time complexity, while extending spectral embeddings to unseen samples is non-trivial and prevents model reuse. In light of this, we propose a Deep Spectral Clustering Network for Incomplete Multi-view Clustering (DSCN-IMC). At its core, DSCNIMC jointly learns the approximate spectral embeddings and the soft cluster assignments using a deep neural network in an unsupervised and end-to-end fashion. Specifically, we integrate the graph convolutional layers and manifold loss into our network to achieve robustness against incomplete multi-view data. After that, an orthnormalization layer is exploited to fulfill the orthogonal constraint of spectral embeddings, thereby achieving the lower running time of spectral clustering. Building upon this, a self-supervised clustering module is introduced to obtain cluster assignments of unseen samples and facilitates model reuse. Our method's efficacy is demonstrated through experiments conducted on nine datasets, wherein we compare its performance against nine state-of-the-art baselines.