Symmetric non-negative matrix factorization-based deep representation algorithm for multi-view clustering

doi:10.1016/j.engappai.2025.112738

Symmetric non-negative matrix factorization-based deep representation algorithm for multi-view clustering

2025-10-14

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DOI：10.1016/j.engappai.2025.112738

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Symmetric Non-negative Matrix Factorization (SNMF) shows significant advantages in clustering task due to its unique mathematical properties. However, it still has several key limitations: (1) the single optimization scheme of traditional multiplicative update rule limits the flexibility of the algorithm; (2) linear factorization leads to insufficient representation ability for complex nonlinear features; (3) lack of learning rate guidance mechanism. These factors together constrain the algorithm representation learning ability in complex data. To address these issues, this paper proposes a SNMF-based Deep Representation algorithm for Multi-view Clustering (SNDRMvC). First, the matrix elements are decoupled, and the stochastic gradient descent as well as nonlinear activation function are used to implement non-negative matrix update. Then, based on the corresponding gradients of the elements and nonlinear function, the neural network learning mechanism is introduced into the SNMF update rule to construct a novel framework SNMF-based deep representation network for optimizing SNMF. This network aims to update the elements in the low-dimensional matrix of each view and fuse the low-dimensional matrices of multiple views to derive a consensus matrix. Finally, extensive experiments conducted on several public datasets demonstrate that the proposed algorithm exhibits notable advantages in clustering performance. We provide the code at: https://github.com/Code706/SNDRMvC .

Symmetric non-negative matrix factorization-based deep representation algorithm for multi-view clustering

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