Emerging Healthcare Problems in High-Dimensional Data and Dimension Reduction

2021 ◽  
pp. 25-49
Author(s):  
Sudhansu Shekhar Patra ◽  
G. M. Harshvardhan ◽  
Mahendra Kumar Gourisaria ◽  
Jnyana Ranjan Mohanty ◽  
Subham Choudhury
Keyword(s):  
Dimension Reduction ◽  
High Dimensional Data ◽  
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.

scholarly journals Adaptive dimension reduction for clustering high dimensional data

2002 ◽  
Author(s):  
Chris Ding ◽  
Xiaofeng He ◽  
Hongyuan Zha ◽  
Horst Simon
Keyword(s):  
Dimension Reduction ◽  
High Dimensional Data ◽  
High Dimensional

Sparse sufficient dimension reduction with heteroscedasticity

2021 ◽  
pp. 2150037
Author(s):  
Haoyang Cheng ◽  
Wenquan Cui
Keyword(s):  
Data Analysis ◽  
Dimension Reduction ◽  
High Dimensional Data ◽  
Real Data ◽  
Dimensional Subspace ◽  
High Dimensional ◽  
Sufficient Dimension Reduction ◽  
Lasso Regression ◽  
High Dimension Data ◽  
Reduction Problem

Heteroscedasticity often appears in the high-dimensional data analysis. In order to achieve a sparse dimension reduction direction for high-dimensional data with heteroscedasticity, we propose a new sparse sufficient dimension reduction method, called Lasso-PQR. From the candidate matrix derived from the principal quantile regression (PQR) method, we construct a new artificial response variable which is made up from top eigenvectors of the candidate matrix. Then we apply a Lasso regression to obtain sparse dimension reduction directions. While for the “large [Formula: see text] small [Formula: see text]” case that [Formula: see text], we use principal projection to solve the dimension reduction problem in a lower-dimensional subspace and projection back to the original dimension reduction problem. Theoretical properties of the methodology are established. Compared with several existing methods in the simulations and real data analysis, we demonstrate the advantages of our method in the high dimension data with heteroscedasticity.

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