关键词: 图像聚类, 半监督学习, 非负矩阵分解, 图正则化

Abstract: This paper presents a novel algorithm called Graph regularized-based Semi-supervised NMF（GSNMF）. It overcomes the shortcomings which ignore the geometric structure and the label information of the data for Non-negative Matrix Factorization（NMF）, Constrained NMF（CNMF） and Graphed regularized NMF（GNMF）. Moreover, those algorithms are special case of GSNMF. The convergence proof of this algorithm is provided. GSNMF preserves the intrinsic geometry of data and uses the label information as semi-supervised learning. It makes nearby samples with the same class-label more compact, and nearby classes separated. Compared with NMF, LNMF, PNMF, GNMF and CNMF, experiment results on ORL face database, FERET face database and USPS handwrite database have shown that the proposed method achieves better clustering results.

Key words: image clustering, semi-supervised learning, Non-negative Matrix Factorization（NMF）, graph regularized