基于深度信念网络和线性单分类SVM的高维异常检测

李昊奇; 应娜; 郭春生; 王金华

doi:10.11959/j.issn.1000-0801.2018006

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基于深度信念网络和线性单分类SVM的高维异常检测

研究与开发 | 更新时间：2024-06-05

- 基于深度信念网络和线性单分类SVM的高维异常检测
- High-dimensional outlier detection based on deep belief network and linear one-class SVM
- 电信科学 2018年34卷第1期页码：34-42
- 作者机构：
- 作者简介：
  
  [ "李昊奇（1992-），男，杭州电子科技大学硕士生，主要研究方向为深度学习与数据挖掘。" ]
  [ "应娜（1978−），女，博士，杭州电子科技大学副教授、硕士生导师，主要研究方向为信号处理与人工智能。" ]
  [ "郭春生（1971−），男，博士，杭州电子科技大学副教授、硕士生导师，主要研究方向为模式识别与人工智能。" ]
  [ "王金华（1992-），女，杭州电子科技大学硕士生，主要研究方向为深度学习与自然语言处理。" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(61372157);“电子科学与技术”浙江省一流学科A类基金资助项目;Zhejiang Provincial First Class Disciplines:Class A-Electronic Science and Technology(GK178800207001)
- DOI：10.11959/j.issn.1000-0801.2018006
  中图分类号： TP183
- 网络出版日期：2018-01，
  
  纸质出版日期：2018-01-20
- 稿件说明：
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李昊奇, 应娜, 郭春生, 等. 基于深度信念网络和线性单分类SVM的高维异常检测[J]. 电信科学, 2018,34(1):34-42.

Haoqi LI, Na YING, Chunsheng GUO, et al. High-dimensional outlier detection based on deep belief network and linear one-class SVM[J]. Telecommunications science, 2018, 34(1): 34-42.
李昊奇, 应娜, 郭春生, 等. 基于深度信念网络和线性单分类SVM的高维异常检测[J]. 电信科学, 2018,34(1):34-42. DOI： 10.11959/j.issn.1000-0801.2018006.

Haoqi LI, Na YING, Chunsheng GUO, et al. High-dimensional outlier detection based on deep belief network and linear one-class SVM[J]. Telecommunications science, 2018, 34(1): 34-42. DOI： 10.11959/j.issn.1000-0801.2018006.

摘要

针对目前高维数据异常检测存在的困难，提出一种基于深度信念网络和线性单分类支持向量机的高维异常检测算法。该算法首先利用深度信念网络具有良好的特征提取功能，实现高维数据的降维，然后基于线性核函数的单分类支持向量机实现异常检测。选取UCI机器学习库中的高维数据集进行实验，结果表明，该算法在检测正确率和计算复杂度上均有明显优势。与PCA-SVDD算法相比，检测正确率有4.65%的提升。与自动编码器算法相比，其训练和测试时间均有显著下降。

Abstract

Aiming at the difficulties in high-dimensional outlier detection at present

an algorithm of high-dimensional outlier detection based on deep belief network and linear one-class SVM was proposed.The algorithm firstly used the deep belief network which had a good performance in the feature extraction to realize the dimensionality reduction of high-dimensional data

and then the outlier detection was achieved based on a one-class SVM with the linear kernel function.High-dimensional data sets in UCI machine learning repository were selected to experiment

result shows that the algorithm has obvious advantages in detection accuracy and computational complexity.Compared with the PCA-SVDD algorithm

the detection accuracy is improved by 4.65%.Compared with the automatic encoder algorithm

its training time and testing time decrease significantly.

关键词

Keywords

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