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-su...

Full description

Saved in:
Bibliographic Details
Published inarXiv.org
Main Authors Sitnik, Dario, Kopriva, Ivica
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 03.04.2020
Subjects
Ablation
Artificial neural networks
Classification
Clustering
Manifolds
Regularization
Representations
Robustness
Subspace methods
Subspaces
Online AccessGet full text

Cover

More Information
Summary: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.
ISSN:2331-8422