Local linear neighbor reconstruction for multi-view data
Local linear neighbor reconstruction for multi-view data
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DOI：
10.1016/j.patrec.2016.08.002
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发表时间：
2016-12
期刊：
影响因子：
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通讯作者：
Linlin Zong;Xianchao Zhang;Hong Yu;Qianli Zhao;Feng Ding
中科院分区：
文献类型：
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作者：
Linlin Zong;Xianchao Zhang;Hong Yu;Qianli Zhao;Feng Ding
基于图的多视图数据分析是近十年来研究的热点，而多视图相似矩阵是多视图数据分析的基础。现有的多视图相似矩阵构建方法无法同时从多个视图中学习到原始数据空间中的局部几何信息。考虑到合适的相似矩阵是具有类内相似度的分块相似矩阵，利用多个原始数据空间中的局部几何信息来学习相似矩阵更为合理。在本文中，我们提出了在多个视图中使用局部线性邻接构造一个统一的相似矩阵。在每个视图中，相似性矩阵可以用原始空间中每个数据点的邻居的权重来重建。在多个视图中，我们寻求一个统一的相似矩阵，该矩阵由每个视图中的相似矩阵组成。统一的相似矩阵可用于谱聚类、标签传播和其他基于图的学习算法。实验结果表明，使用统一相似矩阵的光谱聚类和标签传播算法优于其他多视图相似矩阵算法，也优于典型的多视图光谱聚类算法和典型的多视图标签传播算法。
Graph based multi-view data analysis has become a hot topic in the past decade, and multi-view similarity matrix is fundamental for such tasks. Existing multi-view similarity matrix construction methods cannot learn local geometrical information in the original data space from multiple views simultaneously. Considering the fact that an appropriate similarity matrix is block-wise with intra-class similarity, it is more reasonable to learn a similarity matrix by using local geometrical information in multiple original data space. In this paper, we propose to construct a unified similarity matrix by using local linear neighbors in multiple views. In each view, the similarity matrix can be reconstructed with the weights of the neighbors of each data point in the original space. In multiple views, we seek for a unified similarity matrix which consists of the similarity matrix in each view. The unified similarity matrix can be used for spectral clustering, label propagation and other graph based learning algorithms. Experimental results show that spectral clustering and label propagation algorithms using the unified similarity matrix outperform those using other multi-view similarity matrices, they also outperform typical multi-view spectral clustering algorithms and typical multi-view label propagation algorithms.