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

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.

Authors

• Jie Li Shanghai Jiao Tong University Shanghai Jiao Tong University (Wuxi) Blockchain Advanced Research Center
• Chaoqian Li

Keywords

• Non-negative matrix factorization
• One-hot encoding
• Semi-supervised
• Clustering