Pivot Selection for Dimension Reduction using Annealing by Increasing Resampling
Pivot Selection for Dimension Reduction using Annealing by Increasing Resampling
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发表时间：
2017
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通讯作者：
Yasunobu Imamura;N. Higuchi;T. Kuboyama;Kouichi Hirata;T. Shinohara
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作者：
Yasunobu Imamura;N. Higuchi;T. Kuboyama;Kouichi Hirata;T. Shinohara
.为了选择一组最优的降维枢轴，如简单地图和基于球划分的草图，我们提出了一种方法命名为增加恢复退火（AIR）。AIR假设每个状态都是使用样本集进行评估的。从一个任意的初始状态开始，AIR通过爬山的方式重复到过渡状态，并对初始大小较小并逐渐增加的重采样集进行评估。实验证明，AIR比传统的方法能找到更好的支点集，比模拟退火在更短的时间内。
. In order to select an optimal set of pivots for dimension reduction, such as Simple-Map and sketches based on ball partitioning, we propose a method named Annealing by Increasing Resampling (AIR, for short). AIR assumes that every state is evaluated by using a sample set. Starting from an arbitrary initial state, AIR repeats to transit states by hill climbing, with evaluating the resampled sets whose size initially is small and gradually increases. Experiments verify that AIR can find better sets of pivots than the conventional method and in shorter time than simulated annealing.