Representative points for location-biased datasets |
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Authors: | Zong-Feng Qi Kai-Tai Fang |
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Institution: | 1. The State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, Luoyang, China;2. Division of Science and Technology, BNU-HKBU United International College, Zhuhai, China |
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Abstract: | Representative points (RPs) are a set of points that optimally represents a distribution in terms of mean square error. When the prior data is location biased, the direct methods such as the k-means algorithm may be inefficient to obtain the RPs. In this article, a new indirect algorithm is proposed to search the RPs based on location-biased datasets. Such an algorithm does not constrain the parameter model of the true distribution. The empirical study shows that such algorithm can obtain better RPs than the k-means algorithm. |
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Keywords: | Good lattice point set Kernel estimator Randomized likelihood sampling Representative point |
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