Set-Oriented Dimension Reduction: Localizing Principal Component Analysis Via Hidden Markov Models

2006 ◽  
pp. 74-85 ◽  
Author(s):  
Illia Horenko ◽  
Johannes Schmidt-Ehrenberg ◽  
Christof Schütte
Keyword(s):  
Principal Component Analysis ◽  
Dimension Reduction ◽  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Principal Component ◽  
Component Analysis

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.

Endpoint Detection in Plasma Etching Using Principal Component Analysis and Expanded Hidden Markov Model

2019 ◽  
Vol 34 (1) ◽  
pp. 943-948 ◽  
Author(s):  
Min-Woo Kim ◽  
Seung-Gyun Kim ◽  
ShuKun Zhao ◽  
Sang Jeen Hong ◽  
Seung-Soo Han
Keyword(s):  
Principal Component Analysis ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Plasma Etching ◽  
Hidden Markov ◽  
Principal Component ◽  
Component Analysis ◽  
Endpoint Detection
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