期刊
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
卷 69, 期 -, 页码 24-35出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.engappai.2017.11.008
关键词
Semi-supervised non-negative matrix factorization; Dual-graph model; Orthogonal constraint; Bi-orthogonal constraints; Cluster
类别
资金
- National Natural Science Foundation of China [61371201, 61773304, 61771376, 61772399]
- National Basic Research Program (973 Program) of China [2013CB329402]
- Natural Science Basic Research in Shaanxi Province of China [2014JM2-1006]
Semi-supervised Non-negative Matrix Factorization (NMF) can not only utilize a fraction of label information, but also effectively learn local information of the objectives, such as documents and faces. Semi-supervised NMF is an efficient technique for dimensionality reduction of high dimensional data. In this paper, we propose a novel semi-supervised NMF, called Dual-graph regularized Non-negative Matrix Factorization with Sparse and Orthogonal constraints (SODNMF). Dual-graph model is added into semi-supervised NMF, and the manifold structures of the data space and the feature space are taken into account simultaneously. In addition, the sparse constraint is used in SODNMF, which can simplify the calculation and accelerate the processing speed. The most important is that SODNMF makes use of bi-orthogonal constraints, which can avoid the non-correspondence between images and basic vectors. Therefore, it can effectively enhance the discrimination and the exclusivity of clustering, and improve the clustering performance. We give the objective function, the iterative updating rules and the convergence proof. Empirical experiments demonstrate encouraging results of our novel algorithm in comparison to four algorithms within some state-of-the-art algorithms through a set of evaluations based on three real datasets. (C) 2017 Elsevier Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据