Open AccessA Dimensionality Reduction Model for Complex Data Grouping
Author(s) -
Yang Xiang,
Xiaojun Chen,
Luo Chen
Publication year - 2019
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 - 10.1088/1742-6596/1229/1/012052
Subject(s) - dimensionality reduction , curse of dimensionality , measure (data warehouse) , a priori and a posteriori , inference , computer science , reduction (mathematics) , network packet , simple (philosophy) , convergence (economics) , data mining , algorithm , artificial intelligence , pattern recognition (psychology) , mathematics , geometry , computer network , philosophy , epistemology , economics , economic growth
Given the existing packet dimensionality reduction model, the simple distance hypothesis is only used as a simple assumption that there is a certain relationship between packet data. This document proposes to share information between packet data with relevant random measures as a priori. We explicitly calculated the Lévy measure of the mixed random measure and offered the inference steps of detailed parameter a posteriori. Compared with the traditional method, the grouping dimensionality reduction model can achieve faster convergence and can well maintain the original information of data. The experimental results on the public dataset show that the grouping dimensionality reduction model is an effective dimensionality reduction algorithm and can be employed to extract characteristics on big data.