A novel dimension reduction and dictionary learning framework for high-dimensional data classification

2021 ◽  
Vol 112 ◽  
pp. 107793
Author(s):  
Yanxia Li ◽  
Yi Chai ◽  
Han Zhou ◽  
Hongpeng Yin
Keyword(s):  
Dimension Reduction ◽  
Dictionary Learning ◽  
High Dimensional Data ◽  
Data Classification ◽  
High Dimensional ◽  
Learning Framework

A modular eigen subspace scheme for high-dimensional data classification

2004 ◽  
Vol 20 (7) ◽  
pp. 1131-1143 ◽  
Author(s):  
Yang-Lang Chang ◽  
Chin-Chuan Han ◽  
Fan-Di Jou ◽  
Kuo-Chin Fan ◽  
K.S. Chen ◽  
...  
Keyword(s):  
High Dimensional Data ◽  
Data Classification ◽  
High Dimensional

High-Dimensional Data Dimension Reduction Based on KECA

2013 ◽  
Vol 303-306 ◽  
pp. 1101-1104 ◽  
Author(s):  
Yong De Hu ◽  
Jing Chang Pan ◽  
Xin Tan
Keyword(s):  
Principal Component Analysis ◽  
Dimension Reduction ◽  
High Dimensional Data ◽  
Principal Component ◽  
Good Method ◽  
Component Analysis ◽  
Renyi Entropy ◽  
Rényi Entropy ◽  
Kernel Principal Component Analysis ◽  
High Dimensional

Kernel entropy component analysis (KECA) reveals the original data’s structure by kernel matrix. This structure is related to the Renyi entropy of the data. KECA maintains the invariance of the original data’s structure by keeping the data’s Renyi entropy unchanged. This paper described the original data by several components on the purpose of dimension reduction. Then the KECA was applied in celestial spectra reduction and was compared with Principal Component Analysis (PCA) and Kernel Principal Component Analysis (KPCA) by experiments. Experimental results show that the KECA is a good method in high-dimensional data reduction.

An efficient random forests algorithm for high dimensional data classification

2018 ◽  
Vol 12 (4) ◽  
pp. 953-972 ◽  
Author(s):  
Qiang Wang ◽  
Thanh-Tung Nguyen ◽  
Joshua Z. Huang ◽  
Thuy Thi Nguyen
Keyword(s):  
Random Forests ◽  
High Dimensional Data ◽  
Data Classification ◽  
High Dimensional
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