Article in Press Pattern Recognition Multiple View Semi-supervised Dimensionality Reduction
Article in Press Pattern Recognition Multiple View Semi-supervised Dimensionality Reduction
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通讯作者：
Chenping Hou;Changshui Zhang;Yi Wu;F. Nie
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作者：
Chenping Hou;Changshui Zhang;Yi Wu;F. Nie
模式识别()--在各种数据挖掘应用程序中，会出现多个视图数据，以及以成对约束形式出现的一些领域知识。如何在低维空间中学习隐藏的共识模式是一个具有挑战性的问题。本文提出了一种新的多视点半监督降维方法。利用成对约束导出嵌入到每个视图中，同时引入线性变换，使来自不同模式空间的不同嵌入具有可比性。因此，共识模式可以从多个表示的多个嵌入中学习。我们推导了一个迭代算法来解决上述问题。文中还提供了一些理论分析和样本外扩展。在不同的数据集上进行了有前景的实验，并进行了一些重要的讨论，以证明所提算法的有效性。
Pattern Recognition ()-Multiple view data, together with some domain knowledge in the form of pairwise constraints, arise in various data mining applications. How to learn a hidden consensus pattern in the low dimensional space is a challenging problem. In this paper, we propose a new method for multiple view semi-supervised dimensionality reduction. The pairwise constraints are used to derive embedding in each view and simultaneously , the linear transformation is introduced to make different embeddings from different pattern spaces comparable. Hence, the consensus pattern can be learned from multiple embeddings of multiple representations. We derive an iterating algorithm to solve the above problem. Some theoretical analyses and out-of-sample extensions are also provided. Promising experiments on various data sets, together with some important discussions, are also presented to demonstrate the effectiveness of the proposed algorithm.