Multi-layer manifold learning for deep non-negative matrix factorization-based multi-view clustering

Highlights：

• An orthogonal deep non-negative matrix factorization (Deep-NMF) framework that aims to learn the non-linear parts-based representation for multi-view data is proposed.

• The T-SNE visualizations of the features learned by the proposed Deep-NMF and its counterpart ascertain the effectiveness of the proposed framework for multi-view clustering.

• The proposed Deep-NMF method learns and incorporates the most consensed manifold for multi-view data in all layers of the multi-layer architecture.

• The objective function is designed to uncover the consensus representation that is unique and encodes both the view-shared, view-specific information for multi-view data.

• Extensive experiments including features visualization, components-based and multi-layer ability analysis, comprehensive examples have been conducted and presented in this work.