Open AccessApplication of Text Dimensionality Reduction Method in Information Filtering on Railway Transit Cloud Platform
Author(s) -
Ye Qiu,
Gang Li,
Junfeng An
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1813/1/012058
Subject(s) - dimensionality reduction , cloud computing , computer science , redundancy (engineering) , curse of dimensionality , cluster analysis , data mining , feature selection , feature vector , vector space model , feature extraction , space (punctuation) , artificial intelligence , pattern recognition (psychology) , operating system
This paper focuses on the useful information data extraction of urban rail transportation’s urban rail cloud platform. Through the correlation and redundancy between feature items in the text vector space of a large amount of information data, a method of clustering text vector space dimensionality reduction is proposed and applied to the information data extraction of urban rail cloud platform. The vector aggregation theory and feature selection are applied to reduce the dimensionality of the feature space, so that the reduced dimensional feature items are more representative of the category. Through test experiments, it is proved that this text dimensionality reduction method algorithm is applied to the information data filtering of urban rail cloud platform, which effectively reduces the dimensionality of vector space and improves the accuracy and speed of text classification in urban rail cloud platform information data.