Orthogonal Graph Regularized Nonnegative Matrix Factorization for Image Clustering

  • Jinrong He
  • Dongjian HeEmail author
  • Bin Liu
  • Wenfa Wang
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1120)


Since high-dimensional data can be represented as vectors or matrices, matrix factorization is a common useful data modeling technique for high-dimensional feature representation, which has been widely applied in feature extraction, image processing and text clustering. Graph regularized nonnegative matrix factorization (GNMF) incorporates the non-negativity constraint and manifold regularization simultaneously to achieve a parts-based meaningful high-dimensional data representation, which can discover the underlying local geometrical structure of the original data space. In order to reduce the redundancy between bases and representations, and enhance the clustering power of NMF, three orthogonal variants of GNMF are proposed, which incorporates the orthogonal constraints into GNMF model. The optimization algorithms are developed to solve the objective functions of Orthogonal GNMF (OGNMF). The extensive experimental results on four real-world face image data sets have confirmed the effectiveness of the proposed OGNMF methods.


Image representation Nonnegative Matrix Factorization Manifold regularization Orthogonal projection 



This work was partially supported by National Natural Science Foundation of China (61902339, 61602388), China Postdoctoral Science Foundation (2018M633585, 2017M613216), Natural Science Basic Research Plan in Shaanxi Province of China (2018JQ6060, 2017JM6059), Fundamental Research Funds for the Central Universities (2452019064), Key Research and Development Program of Shaanxi (2019ZDLNY07-06-01), and the Doctoral Starting up Foundation of Yan’an University (YDBK2019-06).


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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Jinrong He
    • 1
    • 2
    • 3
  • Dongjian He
    • 1
    • 3
    Email author
  • Bin Liu
    • 1
    • 3
  • Wenfa Wang
    • 2
  1. 1.Key Laboratory of Agricultural Internet of ThingsMinistry of Agriculture and Rural Affairs, Northwest A&F UniversityYanglingChina
  2. 2.College of Mathematics and Computer ScienceYan’an UniversityYan’anChina
  3. 3.Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent ServiceNorthwest A&F UniversityYanglingChina

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