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Intell. Syst. Technol."],"published-print":{"date-parts":[[2018,1,31]]},"abstract":"<jats:p>Nonnegative matrix factorization (NMF) is one of the most popular data representation methods in the field of computer vision and pattern recognition. High-dimension data are usually assumed to be sampled from the submanifold embedded in the original high-dimension space. To preserve the locality geometric structure of the data,<jats:italic>k<\/jats:italic>-nearest neighbor (<jats:italic>k<\/jats:italic>-NN) graph is often constructed to encode the near-neighbor layout structure. However,<jats:italic>k<\/jats:italic>-NN graph is based on Euclidean distance, which is sensitive to noise and outliers. In this article, we propose a refined-graph regularized nonnegative matrix factorization by employing a manifold regularized least-squares regression (MRLSR) method to compute the refined graph. In particular, each sample is represented by the whole dataset regularized with \u2113<jats:sub>2<\/jats:sub>-norm and Laplacian regularizer. Then a MRLSR graph is constructed based on the representative coefficients of each sample. Moreover, we present two optimization schemes to generate refined-graphs by employing a hard-thresholding technique. We further propose two refined-graph regularized nonnegative matrix factorization methods and use them to perform image clustering. Experimental results on several image datasets reveal that they outperform 11 representative methods.<\/jats:p>","DOI":"10.1145\/3090312","type":"journal-article","created":{"date-parts":[[2017,8,24]],"date-time":"2017-08-24T11:49:04Z","timestamp":1503575344000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":11,"title":["Refined-Graph Regularization-Based Nonnegative Matrix Factorization"],"prefix":"10.1145","volume":"9","author":[{"given":"Xuelong","family":"Li","sequence":"first","affiliation":[{"name":"Chinese Academy of Sciences, Shaanxi, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guosheng","family":"Cui","sequence":"additional","affiliation":[{"name":"Chinese Academy of Sciences, Shaanxi, P. R. 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