Clean and robust multi-level subspace representations learning for deep multi-view subspace clustering

Deep multi -view subspace clustering has achieved increasing attention owing to its encouraging ability to address the nonlinear multi -view data. Despite significant progress, most existing deep multi -view subspace clustering methods still suffer from the following drawbacks. First, they usually focus on utilizing the last -level feature from the last encoder layer of the auto -encoder related to each view, yet often overlook exploiting the earlier -level features from the earlier encoder layers as well as integrating the multi -level features from multiple encoder layers. Second, when learning the subspace representation from the self -expressive layer, they rarely consider the cleanliness and robustness of the learned subspace representation. To overcome these drawbacks, this paper proposes a novel c lean and r obust m ulti -level s ubspace r epresentations l earning for deep multi -view subspace clustering (CRMSRL). Specifically, the auto -encoder is adopted to nonlinearly map each view into the multi -level features, and then multiple self -expressive layers are added between the encoder layers and their corresponding decoder layers to provide multiple information flow paths through the auto -encoder and learn the multi -level subspace representations corresponding to the multi -level features. Consequently, the intricate hierarchical information embedded in the multi -level features is passed to the corresponding multilevel subspace representations. Moreover, with the idea of robust principal component analysis (RPCA), the learned multi -level subspace representations can be further purified to achieve the cleaner and more robust ones. In addition, to excavate the structural consistency among different views, the common layer is designed to interact with different view -specific self -expressive layers and achieve the high -quality common subspace representation, which can be applied to the spectral clustering algorithm to obtain the final clustering results. Experimental results show that CRMSRL outperforms twelve state-of-the-art baseline methods on six benchmark datasets. In particular, CRMSRL achieves 4.5% improvements on the MSRCV1 dataset in terms of accuracy, compared with the best baseline method.