Adaptive metric nearest neighbor classification
Adaptive metric nearest neighbor classification
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DOI：
10.1109/cvpr.2000.855863
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发表时间：
2000
期刊：
影响因子：
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通讯作者：
C. Domeniconi;D. Gunopulos;Jing Peng
中科院分区：
文献类型：
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作者：
C. Domeniconi;D. Gunopulos;Jing Peng
最近邻分类假设局部恒定的类条件概率。由于维数灾难，这种假设在有限样本的高维中变得无效。当使用最近邻规则时，在这些条件下可以引入严重的偏差。我们提出了一种局部自适应最近邻分类方法，试图最大限度地减少偏见。我们使用卡方距离分析来计算一个灵活的指标，用于产生高度适应查询位置的邻域。邻域沿沿着不太相关的特征尺寸被拉长，而沿沿着最有影响的特征尺寸被收缩。其结果是，类条件概率往往是更平滑的修改后的邻域，从而可以实现更好的分类性能。我们的方法的有效性进行了验证，并与其他技术使用各种模拟和真实的世界的数据进行比较。
Nearest neighbor classification assumes locally constant class conditional probabilities. This assumption becomes invalid in high dimensions with finite samples due to the curse of dimensionality. Severe bias can be introduced under these conditions when using the nearest neighbor rule. We propose a locally adaptive nearest neighbor classification method to try to minimize bias. We use a Chi-squared distance analysis to compute a flexible metric for producing neighborhoods that are highly adaptive to query locations. Neighborhoods are elongated along less relevant feature dimensions and constricted along most influential ones. As a result, the class conditional probabilities tend to be smoother in the modified neighborhoods, whereby better classification performance can be achieved. The efficacy of our method is validated and compared against other techniques using a variety of simulated and real world data.