Target Neighbor Consistent Feature Weighting for Nearest Neighbor Classification-猫眼课题宝

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2011-12

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作者：

I. Takeuchi;Masashi Sugiyama

我们考虑了最近邻分类器的特征选择和加权。在这种情况下的一个技术挑战是如何处理在学习过程中特征空间度量发生变化时最近邻的离散更新。这个问题被称为目标邻居变化，在现有的特征加权和度量学习文献中没有得到适当的解决。在本文中，我们提出了一种新的特征加权算法，该算法可以通过顺序二次规划准确有效地跟踪正确的目标邻居。据我们所知，这是第一个保证目标邻居和特征空间度量之间一致性的算法。我们进一步表明，该算法可以自然地与正则化路径跟踪相结合，允许计算效率高的正则化参数选择。通过实验验证了该算法的有效性。

We consider feature selection and weighting for nearest neighbor classifiers. A technical challenge in this scenario is how to cope with discrete update of nearest neighbors when the feature space metric is changed during the learning process. This issue, called the target neighbor change, was not properly addressed in the existing feature weighting and metric learning literature. In this paper, we propose a novel feature weighting algorithm that can exactly and efficiently keep track of the correct target neighbors via sequential quadratic programming. To the best of our knowledge, this is the first algorithm that guarantees the consistency between target neighbors and the feature space metric. We further show that the proposed algorithm can be naturally combined with regularization path tracking, allowing computationally efficient selection of the regularization parameter. We demonstrate the effectiveness of the proposed algorithm through experiments.

Target Neighbor Consistent Feature Weighting for Nearest Neighbor Classification

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