Robust Multiview Subspace Learning With Nonindependently and Nonidentically Distributed Complex Noise
Publication in refereed journal

香港中文大學研究人員
替代計量分析
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其它資訊
摘要Multiview Subspace Learning (MSL), which aims at obtaining a low-dimensional latent subspace from multiview data, has been widely used in practical applications. Most recent MSL approaches, however, only assume a simple independent identically distributed (i.i.d.) Gaussian or Laplacian noise for all views of data, which largely underestimates the noise complexity in practical multiview data. Actually, in real cases, noises among different views generally have three specific characteristics. First, in each view, the data noise always has a complex configuration beyond a simple Gaussian or Laplacian distribution. Second, the noise distributions of different views of data are generally nonidentical and with evident distinctiveness. Third, noises among all views are nonindependent but obviously correlated. Based on such understandings, we elaborately construct a new MSL model by more faithfully and comprehensively considering all these noise characteristics. First, the noise in each view is modeled as a Dirichlet process (DP) Gaussian mixture model (DPGMM), which can fit a wider range of complex noise types than conventional Gaussian or Laplacian. Second, the DPGMM parameters in each view are different from one another, which encodes the ``nonidentical'' noise property. Third, the DPGMMs on all views share the same high-level priors by using the technique of hierarchical DP, which encodes the ``nonindependent'' noise property. All the aforementioned ideas are incorporated into an integrated graphics model which can be appropriately solved by the variational Bayes algorithm. The superiority of the proposed method is verified by experiments on 3-D reconstruction simulations, multiview face modeling, and background subtraction, as compared with the current state-of-the-art MSL methods.
出版社接受日期19.06.2019
著者Yue Zongsheng, Yong Hongwei, Meng Deyu, Zhao Qian, Leung Yee, Zhang Lei
期刊名稱IEEE Transactions on Neural Networks and Learning Systems
出版年份2020
月份4
卷號31
期次4
出版社IEEE
頁次1070 - 1083
國際標準期刊號2162-237X
電子國際標準期刊號2162-2388
語言美式英語
關鍵詞Data models, Laplace equations, Adaptation models, Distributed databases, Feature extraction, Correlation, Robustness, Dirichlet process (DP) mixture model, hierarchical Dirichlet process (HDP), multiview, subspace learning, variational Bayes

上次更新時間 2020-03-08 於 04:14