苏志勋

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教授

博士生导师

硕士生导师

性别:男

毕业院校:大连理工大学

学位:博士

所在单位:数学科学学院

学科:计算数学

办公地点:创新园大厦(海山楼)B1313

联系方式:84708351-8093

电子邮箱:zxsu@dlut.edu.cn

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Bayesian Low-Rank and Sparse Nonlinear Representation for Manifold Clustering

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论文类型:期刊论文

第一作者:Tang, Kewei

通讯作者:Tang, KW (reprint author), Liaoning Normal Univ, Sch Math, 850 Huanghe Rd, Dalian 116029, Liaoning, Peoples R China.; Su, ZX (reprint author), Dalian Univ Technol, Sch Math Sci, 2 Linggong Rd, Dalian 116024, Liaoning, Peoples R China.

合写作者:Zhang, Jie,Su, Zhixun,Dong, Jiangxin

发表时间:2016-12-01

发表刊物:NEURAL PROCESSING LETTERS

收录刊物:SCIE、EI、Scopus

卷号:44

期号:3

页面范围:719-733

ISSN号:1370-4621

关键字:Bayes; Low-rank; Manifold; Nonlinear representation; Sparse

摘要:Linear representation usually used by the optimization model about low-rankness and sparsity limits their applications to some extent. In this paper, we propose Bayesian low-rank and sparse nonlinear representation (BLSN) model exploiting nonlinear representation. Different from the optimization model, BLSN can be solved by traditional algorithm in Bayesian statistics easily without knowing the explicit mapping by kernel trick. Moreover, it can learn the parameters adaptively to choose the low-rank and sparse properties and also provides a way to enforce more properties on one quantity in a Bayesian model. Based on the observation that the data points drawn from a union of manifolds may gain more meaningful linear structure after a nonlinear mapping, we apply BLSN for manifold clustering. It can handle different problems by constructing various kernels. With respect to the case of linear manifold, known as subspace segmentation, we propose a kernel by the Veronese mapping. In addition, we also design the kernel matrices for the case of nonlinear manifold. Experimental results confirm the effectiveness and the potential of our model for manifold clustering.