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Chaoqian Li

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EAAI Journal 2025 Journal Article

Elements discriminative non-negative matrix factorization for data clustering

  • Jie Li
  • Xuzhu Shen
  • Chaoqian Li
  • Yaotang Li

Semi-supervised non-negative matrix factorization (NMF) is widely used in data clustering because it can generate more discriminative representations for high-dimensional data by leveraging partial label information. To advance research in this field, we propose a novel method, Element Discriminative NMF (EDNMF), which incorporates discrimination constraints based on the element ratio and element difference of the new representations of labeled data points. EDNMF is implemented in two variants, each with an iterative algorithm for solving the optimization problem. We further analyze the computational complexity and convergence of these algorithms. A key advantage of EDNMF is that its learned representations can serve directly as a clustering assignment matrix, thereby simplifying the clustering process. Extensive experiments on eight real-world datasets demonstrate that EDNMF consistently outperforms baseline methods, confirming its effectiveness in improving clustering performance. The code is available at https: //github. com/ljisxz/EDNMF.

Details DOI

EAAI Journal 2025 Journal Article

Graph regularized One-hot-constrained nonnegative matrix factorization for data representation

  • Jie Li
  • Chaoqian Li

Non-negative matrix factorization (NMF) effectively reduces the dimensionality of high-dimensional data. The low-dimensional representations obtained by NMF are widely used in computer vision, information retrieval, and pattern recognition. Semi-supervised NMF can obtain more discriminative low-dimensional representation by incorporating partial label information. In this paper, we propose a semi-supervised NMF method called Graph regularized One-hot Constrained NMF(GOCNMF), by setting the low-dimensional representations of the labeled data points as One-hot vectors in the decomposition. The setting guarantees the clustering assignments of labeled data points are consistent with their ground truth classes. In clustering tasks, the low-dimensional representations of the unlabeled data points are guided by the graph regularization to approximate the One-hot vectors in new space, so that the low-dimensional representations generated by GOCNMF can directly be used as a clustering assignment matrix. In addition, the setting can also be considered as an initialization strategy for the optimization algorithm of GOCNMF, which can accelerate the convergence speed of the algorithm and reduce its computational complexity. The clustering experiments on six real datasets show that our proposed GOCNMF outperforms the comparison methods overall, which validates the effectiveness of our method. The code for reproducing our results can be obtained at: https: //github. com/ljisxz/GOCNMF.

Details DOI
v2026.09.13