Robust Multiview Subspace Learning With Nonindependently and Nonidentically Distributed Complex Noise
Robust Multiview Subspace Learning With Nonindependently and Nonidentically Distributed Complex Noise
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具有非独立和不同分布的复杂噪声的鲁棒多视图子空间学习
DOI：
10.1109/tnnls.2019.2917328
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发表时间：
2020-04
影响因子：
10.4
通讯作者：
Yee Leung
中科院分区：
文献类型：
--
作者：
Zongsheng Yue;Hongwei Yong;Deyu Meng;Qian Zhao;Yee Leung
多视图子空间学习(MSL)旨在从多个视点数据中获取低维的潜在子空间，在实际应用中得到了广泛的应用。然而，大多数最新的MSL方法只假设一个简单的独立同分布(I.I.D.)所有数据视图的高斯或拉普拉斯噪声，这在很大程度上低估了实际多视图数据中的噪声复杂性。事实上，在实际案例中，不同观点之间的噪音通常有三个具体的特征。首先，在每个视图中，数据噪声总是具有除简单的高斯或拉普拉斯分布之外的复杂结构。第二，不同视角数据的噪声分布一般是不同的，且具有明显的区分性。第三，所有观点中的噪声不是独立的，但存在明显的相关性。基于这些认识，我们更真实、更全面地考虑这些噪声特性，精心构建了一种新的MSL模型。首先，将每个视点中的噪声建模为Dirichlet过程(DP)高斯混合模型(DPGMM)，该模型比传统的高斯或拉普拉斯模型能够适应更广泛的复杂噪声类型。其次，每个视图中的DPGMM参数彼此不同，这编码了“不相同”的噪声属性。第三，所有视图上的DPGMM通过使用分层DP技术共享相同的高级先验知识，该技术对“非独立”噪声属性进行编码。所有上述思想都被结合到一个综合的图形模型中，该模型可以通过变分贝叶斯算法进行适当的求解。通过三维重建仿真、多视点人脸建模和背景减影实验验证了该方法的优越性，并与现有的MSL方法进行了比较。
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.
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影响因子：
3.7
作者：
T. Burr
通讯作者：
T. Burr
复杂多波段噪声下的高光谱图像恢复
影响因子：
5
作者：
Zongsheng Yue;Deyu Meng;Yongqing Sun;Qian Zhao
通讯作者：
Qian Zhao
DOI：
10.1214/12-aoas597
发表时间：
2013-03-01
期刊：
The annals of applied statistics
影响因子：
--
作者：
Lock EF;Hoadley KA;Marron JS;Nobel AB
通讯作者：
Nobel AB
影响因子：
0.8
作者：
I. Mizera;C. Müller
通讯作者：
I. Mizera;C. Müller
基于克罗内克基表示的张量稀疏性及其在张量恢复中的应用
DOI：
10.1109/tpami.2017.2734888
发表时间：
2018
影响因子：
23.6
作者：
Xie Qi;Zhao Qian;Meng Deyu;Xu Zongben
通讯作者：
Xu Zongben