[1]ZHAO Guanghua,YANG Tao,FU Dongmei.Incremental dimensionality reduction algorithm based on data manifold boundaries and distribution state[J].CAAI Transactions on Intelligent Systems,2023,18(5):975-983.[doi:10.11992/tis.202205007]
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Incremental dimensionality reduction algorithm based on data manifold boundaries and distribution state

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 18 Number of periods: 2023 5 Page number: 975-983 Column: 学术论文—机器学习 Public date: 2023-09-05
Title:
Incremental dimensionality reduction algorithm based on data manifold boundaries and distribution state
Author(s):
ZHAO Guanghua1;  YANG Tao1; 2;  FU Dongmei1; 2
1. School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China;
2. Shunde Innovation School, University of Science and Technology Beijing, Foshan 528300, China
Keywords:
incremental learning; manifold dimension reduction; noise; manifold boundary; probability distribution; projection; outlier detection; classification
CLC:
TP391
DOI:
10.11992/tis.202205007
Abstract:
To eliminate the impact of noise on incremental manifold learning and conduct manifold dimensionality reduction on new data under different distribution states, an incremental dimensionality reduction algorithm is proposed based on data manifold boundaries and distribution state. In the algorithm, the probability distribution of noises is analyzed while simultaneously performing data noise reduction. The manifold shape of the data with noise reduction is determined as the main manifold, wherein the distribution form of noise is represented to obtain the approximate manifold boundary of the original data. Subsequently, the distribution state of the new data is determined based on the manifold boundary. Finally, the new data distributed inside and outside the original manifold shape are mapped to the low-dimensional space. Experiments reveal that the algorithm can effectively achieve the excavation of the low-dimensional features of incremental high-dimensional noisy data based on manifold learning.
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