Multi-layer manifold learning for deep non-negative matrix factorization-based multi-view clustering - Luong, Khanh; Nayak, Richi; Balasubramaniam, Thirunavukarasu; Bashar, Md Abul (Pattern recognition, 2022)
•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. Multi-view data clustering based on Non-negative Matrix Factorization (NMF) has been commonly used for pattern recognition by grouping multi-view high-dimensional data by projecting it to a lower-order dimensional space. However, the NMF framework fails to learn the accurate lower-order representation of the input data if it exhibits complex and non-linear relationships. This paper proposes a deep non-negative matrix factorization-based framework for effective multi-view data clustering by uncovering both the non-linear relationships and the intrinsic components of the data. Both the consensus and complementary information present in multiple views are sufficiently learned in the proposed framework with the effective use of constraints such as normalized cut-type and orthogonal. The optimal manifold of multi-view data is effectively incorporated in all layers of the framework. Extensive experimental results show the proposed method outperforms state-of-the-art multi-view matrix factorization-based methods.