1 research outputs found
Robust Self-Supervised Convolutional Neural Network for Subspace Clustering and Classification
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 (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. 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 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.Comment: 15 pages, 3 tables, 3 figure