Open AccessHandwritten Digital Image Classification Based on PCA Dimensionality Reduction
Author(s) -
Xingxing Li,
Chao Duan,
Zhi Yan,
Panpan Yin
Publication year - 2020
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/440/4/042071
Subject(s) - dimensionality reduction , computer science , artificial intelligence , pattern recognition (psychology) , reduction (mathematics) , nonlinear dimensionality reduction , machine learning , curse of dimensionality , clustering high dimensional data , data mining , mathematics , cluster analysis , geometry
Dimensionality reduction is an important idea in machine learning. In the machine learning, some high-dimensional data sets are often encountered. In the case of high-dimensional data, data samples are sparse and distance calculations are difficult. Such problems are serious problems faced by all machine learning methods. “Dimensional disaster.” In addition, linear correlation between features is easy to occur in high-dimensional features, which means that some features are redundant.