一种自适应的高维离群点识别方法

An Adaptive High-Dimensional Outlier Recognition Method

针对传统的基于距离的离群点识别方法难以直接有效地应用于高维数据且识别效果受参数影响的问题,本文提出了一种自适应的高维离群点识别方法,该方法利用经遗传算法优化的高斯受限玻尔兹曼机将高维数据非线性地映射到低维空间,然后通过自适应的离群点识别方法在低维数据空间进行离群点识别。采用UCI高维数据和中药高维数据进行验证实验,实验结果表明自适应的高维离群点识别方法能自适应地、有效地识别出高维数据中的离群点。

Aiming at the problem that the traditional distance-based outlier recognition method can not be directly and effectively applied to high-dimensional data and the recognition effect is affected by parameters, an adaptive high-dimensional outlier recognition method is proposed, which uses genetic algorithm. The optimized Gaussian Restricted Boltzmann machine nonlinearly maps high-dimensional data to low-dimensional space, and then performs outlier recognition in low-dimensional data space by adaptive outlier recognition. UCI high-dimensional data and high-dimensional data of traditional Chinese medicine are used to verify the experiment. The experimental results show that the adaptive high-dimensional outlier recognition method can adaptively and effectively identify outliers in high-dimensional data.