Linear Semi-Supervised Dimensionality Reduction with Pairwise Constraint for Multiple Subclasses
Linear Semi-Supervised Dimensionality Reduction with Pairwise Constraint for Multiple Subclasses
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DOI：
10.1587/transinf.e95.d.812
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发表时间：
2012-03
期刊：
影响因子：
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通讯作者：
Bin Tong;Weifeng Jia;Yanli Ji;Einoshin Suzuki
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文献类型：
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作者：
Bin Tong;Weifeng Jia;Yanli Ji;Einoshin Suzuki
我们提出了一种新方法，称为具有成对约束的面向子类的降维（SODRPaC），用于降维。在高维空间中，通常具有一个类别的一组数据点可能分散在多个不同的组中。当前的线性半监督降维方法将无法实现公平的性能，因为它们假设由必须链接约束链接的两个数据点彼此接近，而它们很可能位于不同的组中。受上述观察的启发，我们将必须链接约束分为两类，分别是子类间必须链接约束和子类内必须链接约束。我们通过使用必须链接约束仔细生成无法链接约束，然后通过使用无法链接约束和共享最近邻的紧凑性提出新的判别标准。流形正则化也被纳入我们的降维框架中。对合成数据集和实际数据集的广泛实验说明了我们方法的有效性。
We propose a new method, called Subclass-oriented Dimensionality Reduction with Pairwise Constraints (SODRPaC), for dimensionality reduction. In a high dimensional space, it is common that a group of data points with one class may scatter in several different groups. Current linear semi-supervised dimensionality reduction methods would fail to achieve fair performances, as they assume two data points linked by a must-link constraint are close each other, while they are likely to be located in different groups. Inspired by the above observation, we classify the must-link constraint into two categories, which are the inter-subclass must-link constraint and the intra-subclass must-link constraint, respectively. We carefully generate cannot-link constraints by using must-link constraints, and then propose a new discriminant criterion by employing the cannot-link constraints and the compactness of shared nearest neighbors. The manifold regularization is also incorporated in our dimensionality reduction framework. Extensive experiments on both synthetic and practical data sets illustrate the effectiveness of our method.