• Complex
  • Title
  • Keyword
  • Abstract
  • Scholars
  • Journal
  • ISSN
  • Conference
成果搜索

author:

Liu, Genggeng (Liu, Genggeng.) [1] (Scholars:刘耿耿) | Xie, Lin (Xie, Lin.) [2] | Chen, Chi-Hua (Chen, Chi-Hua.) [3]

Indexed by:

Scopus SCIE

Abstract:

Dimensionality reduction plays an important role in the data processing of machine learning and data mining, which makes the processing of high-dimensional data more efficient. Dimensionality reduction can extract the low-dimensional feature representation of high-dimensional data, and an effective dimensionality reduction method can not only extract most of the useful information of the original data, but also realize the function of removing useless noise. In this paper, an unsupervised multilayered variational auto-encoder model is studied in the text data, so that the high-dimensional feature to the low-dimensional feature becomes efficient and the low-dimensional feature can retain mainly information as much as possible. Low-dimensional feature obtained by different dimensionality reduction methods are used to compare with the dimensionality reduction results of variational auto-encoder (VAE). Compared with other dimensionality reduction methods, the classification accuracy of VAE on different data sets is improved by at least 0.21% and at most 3.7%.

Keyword:

dimensionality reduction Machine learning text classification unsupervised feature learning variational auto-encoder

Community:

  • [ 1 ] [Liu, Genggeng]Fuzhou Univ, Coll Math & Comp Sci, Room 108,Bldg 2,2 Xueyuan Rd, Fuzhou, Fujian, Peoples R China
  • [ 2 ] [Xie, Lin]Fuzhou Univ, Coll Math & Comp Sci, Room 108,Bldg 2,2 Xueyuan Rd, Fuzhou, Fujian, Peoples R China
  • [ 3 ] [Chen, Chi-Hua]Fuzhou Univ, Coll Math & Comp Sci, Room 108,Bldg 2,2 Xueyuan Rd, Fuzhou, Fujian, Peoples R China

Reprint 's Address:

  • 陈志华

    [Chen, Chi-Hua]Fuzhou Univ, Coll Math & Comp Sci, Room 108,Bldg 2,2 Xueyuan Rd, Fuzhou, Fujian, Peoples R China

Show more details

Version:

Related Keywords:

Related Article:

Source :

INFORMATION TECHNOLOGY AND CONTROL

ISSN: 1392-124X

Year: 2020

Issue: 3

Volume: 49

Page: 421-437

1 . 2 2 8

JCR@2020

2 . 0 0 0

JCR@2023

ESI Discipline: COMPUTER SCIENCE;

ESI HC Threshold:149

JCR Journal Grade:4

CAS Journal Grade:4

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 8

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 2

Online/Total:166/10015171
Address:FZU Library(No.2 Xuyuan Road, Fuzhou, Fujian, PRC Post Code:350116) Contact Us:0591-22865326
Copyright:FZU Library Technical Support:Beijing Aegean Software Co., Ltd. 闽ICP备05005463号-1