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Affiliation of Author(s):计算机科学与技术学院/人工智能学院/软件学院

Journal:PATTERN RECOGNITION

Key Words:Subspace clustering Noises modelling Finite mixture model Nonconvex and nonsmooth optimization

Abstract:Subspace segmentation or clustering remains a challenge of interest in computer vision when handling complex noise existing in high-dimensional data. Most of the current sparse representation or minimum rank based techniques are constructed on l(1)-norm or l(2)-norm losses, which is sensitive to outliers. Finite mixture model, as a class of powerful and flexible tools for modeling complex noise, becomes a must. Among all the choices, exponential family mixture is extremely useful in practice due to its universal approximation ability for any continuous distribution and hence covers a broader scope of characteristics of noise distribution. Equipped with such a modeling idea, this paper focuses on the complex noise contaminated subspace clustering problem by using finite mixture of exponential power (MoEP) distributions. We then harness a penalized likelihood function to perform automatic model selection and hence avoid over-fitting. Moreover, we introduce a novel prior on the singular values of representation matrix, which leads to a novel penalty in our nonconvex and nonsmooth optimization. The parameters of the MoEP model can be estimated with a Maximum A Posteriori (MAP) method. Meanwhile, the subspace is computed with joint weighted l(p)-norm and Schatten-q quasi-norm minimization. Both theoretical and experimental results show the effectiveness of our method. (C) 2019 Elsevier Ltd. All rights reserved.

ISSN No.:0031-3203

Translation or Not:no

Date of Publication:2019-09-01

Co-author:Guo, Xianglin,Xie, Xingyu,Liu, Guangcan,Wang, Jun,Jun Wang

Correspondence Author:Wang, Jun,Mingqiang Wei