Classification Method Based on Dimension Reduction

Teng Shao-hua; Lu Dong-lue; Huo Ying-xiang; Zhang Wei

doi:10.12052/gdutxb.170008

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Classification Method Based on Dimension Reduction

更新时间：2026-05-27

- Classification Method Based on Dimension Reduction
- Vol. 34, Issue 3, Pages: 1-7(2017)
- 作者机构：
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- DOI：10.12052/gdutxb.170008
  CLC： TP391
- Published：2017
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滕少华, 卢东略, 霍颖翔, 张巍. 基于正交投影的降维分类方法研究[J]. 广东工业大学学报, 2017, 34(3): 1-7.

Teng Shao-hua, Lu Dong-lue, Huo Ying-xiang, et al. Classification Method Based on Dimension Reduction[J]. 2017, 34(3): 1-7.
滕少华, 卢东略, 霍颖翔, 张巍. 基于正交投影的降维分类方法研究[J]. 广东工业大学学报, 2017, 34(3): 1-7. DOI： 10.12052/gdutxb.170008.

Teng Shao-hua, Lu Dong-lue, Huo Ying-xiang, et al. Classification Method Based on Dimension Reduction[J]. 2017, 34(3): 1-7. DOI： 10.12052/gdutxb.170008.

摘要

大数据时代要求数据挖掘算法能高效处理海量数据，针对传统分类算法建模时间长、分类耗时久、算法难以理解等问题，提出一种基于正交投影的降维分类方法，通过投影方式将多维分类问题转化为多个二维投影面组合问题，建立投影面的密度模型进行分类.相比于常用的支持向量机（Support Vector Machine，SVM）、逻辑回归（Logistic Regression，LR）、k最近邻（K-Nearest Neighbor，KNN）等分类算法，基于正交投影降维的分类方法能够在不丢失分类准确度的同时，拥有较高的模型训练效率与分类效率.其算法易于实现，可用于实时性要求较高的应用场合，如入侵检测，交通调度等.

Abstract

Data mining algorithm in the era of big data needs to be able to efficiently deal with massive data. Traditional classification algorithms take a long time to train a model and classify the test dataset

and the algorithm is difficult to understand. To deal with the problems

a classification method based on dimension reduction is proposed in this paper. The multidimensional classification problem is transformed into multiple 2D projection surface combination by projection

and a density model of the projection surface is trained for classification. Compared with Support Vector Machines (SVM)

Logistic Regression (LR)

K-Nearest Neighbor (KNN) and other algorithms

the classification method based on dimension reduction has higher training efficiency and classification efficiency without loss of accuracy. The method is easy to implement

so it can be used for real-time application

such as intrusion detection and traffic scheduling.

关键词

Keywords

references

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