Robust Self-Supervised Convolutional Neural Network for Subspace Clustering and Classification

• URL: http://arxiv.org/abs/2004.03375v1
• Date: Fri, 3 Apr 2020 16:07:58 GMT
• Title: Robust Self-Supervised Convolutional Neural Network for Subspace Clustering and Classification
• Authors: Dario Sitnik and Ivica Kopriva
• Abstract summary: This paper proposes the robust formulation of the self-supervised convolutional subspace clustering network ($S2$ConvSCN) In a truly unsupervised training environment, Robust $S2$ConvSCN outperforms its baseline version by a significant amount for both seen and unseen data on four well-known datasets.
• Score: 0.10152838128195464
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Insufficient capability of existing subspace clustering methods to handle data coming from nonlinear manifolds, data corruptions, and out-of-sample data hinders their applicability to address real-world clustering and classification problems. This paper proposes the robust formulation of the self-supervised convolutional subspace clustering network ($S^2$ConvSCN) that incorporates the fully connected (FC) layer and, thus, it is capable for handling out-of-sample data by classifying them using a softmax classifier. $S^2$ConvSCN clusters data coming from nonlinear manifolds by learning the linear self-representation model in the feature space. Robustness to data corruptions is achieved by using the correntropy induced metric (CIM) of the error. Furthermore, the block-diagonal (BD) structure of the representation matrix is enforced explicitly through BD regularization. In a truly unsupervised training environment, Robust $S^2$ConvSCN outperforms its baseline version by a significant amount for both seen and unseen data on four well-known datasets. Arguably, such an ablation study has not been reported before.

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