Discriminative and Graph Regularized Nonnegative Matrix Factorization with Kernel Method

Jisuanji kexue yu tansuo

LI Xiangli, ZHANG Ying¹ 

[1] 1. School of Mathematics and Computational Science, Guilin University of Electronic Technology, Guilin, Guangxi 541004, China 2. Guangxi Key Laboratory of Cryptography and Information Security, Guilin, Guangxi 541004, China 3. Guangxi Key Laboratory of Automatic Testing Technology and Instruments, Guilin, Guangxi 541004, China 4. Guangxi University Key Laboratory of Data Analysis and Calculation, Guilin, Guangxi 541004, China;

关键词: nonnegative matrix factorization (nmf);    semi-supervised clustering;    graph regular;    kernel method;   

DOI  :  10.3778/j.issn.1673-9418.2002041

来源: DOAJ

【 摘 要 】

Nonnegative matrix factorization (NMF) is a popular technique for dimension reduction,which has been extensively applied in image clustering and other fields.However,NMF is an unsupervised approach,which does not take the label information of the data and capture the inherent geometrical structure of data space.And NMF is a linear method that can't be used when the data are nonlinear.To this end,discriminative and graph regularized non-negative matrix factorization with kernel method is proposed,which uses the available label information,incorporates the graph into the NMF to capture the inherent geometrical structure and uses the kernel method to avoid the nonlinear data, and the result of factorization can effectively improve the clustering effect.Iterative initialization of variants of the NMF is random.A “warm start”strategy is adopted to avoid randomness in the result.Clustering experi-ments on several image datasets verify the effectiveness of the algorithm proposed in this paper compared with the other state-of-the-art methods.

【 授权许可】

Unknown