Robust local-coordinate non-negative matrix factorization with adaptive graph for robust clustering

With its unique geometric properties, non-negative matrix factorization (NMF) has become one of the widely used clustering methods in the field of data mining. Regrettably, most existing NMF methods are sensitive to super-noise (super-outliers). This paper proposes a novel robust clustering method to address this issue. Based on the Hx loss function, this method establishes a novel robust adaptive local structure learning strategy, reducing the interference of noise (outliers) on data reconstruction and space exploration. In addition, a new orthogonal regularization term is incorporated into the model, ensuring the orthogo-nality of the factor matrix and enhancing the discriminant ability. Finally, we develop an efficient algorithm to solve the resultant model and analyze its convergence from theoret-ical and experimental aspects. Experimental results on random synthetic data sets and benchmark databases demonstrate that the proposed method outperforms the existing robust NMF methods in terms of spatial structure learning, discriminant power, and robustness.(c) 2022 Elsevier Inc. All rights reserved.

Automated summary

This paper proposes a novel robust clustering method that reduces the interference of noise on data reconstruction and space exploration through a robust adaptive local structure learning strategy and an orthogonal regularization term, enhancing the discriminant ability.