Indexed by:
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:
Reprint 's Address:
Email:
Version:
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:
SCOPUS Cited Count: 8
ESI Highly Cited Papers on the List: 0 Unfold All
WanFang Cited Count:
Chinese Cited Count:
30 Days PV: 2
Affiliated Colleges: