scholarly journals Probabilistic predictive principal component analysis for spatially misaligned and high‐dimensional air pollution data with missing observations

2019 ◽  
Vol 31 (4) ◽  
Author(s):  
Phuong T. Vu ◽  
Timothy V. Larson ◽  
Adam A. Szpiro
Keyword(s):  
Principal Component Analysis ◽  
Air Pollution ◽  
Principal Component ◽  
Component Analysis ◽  
High Dimensional ◽  
Missing Observations

scholarly journals Using principal component analysis for neural network high-dimensional potential energy surface

2020 ◽  
Vol 152 (23) ◽  
pp. 234103
Author(s):  
Bastien Casier ◽  
Stéphane Carniato ◽  
Tsveta Miteva ◽  
Nathalie Capron ◽  
Nicolas Sisourat
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Potential Energy ◽  
Potential Energy Surface ◽  
Energy Surface ◽  
Principal Component ◽  
Component Analysis ◽  
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 Enhanced Supervised Principal Component Analysis for Cancer Classification

2021 ◽  
pp. 1321-1333
Author(s):  
Ghadeer JM Mahdi ◽  
Bayda A. Kalaf ◽  
Mundher A. Khaleel
Keyword(s):  
Principal Component Analysis ◽  
Gradient Descent ◽  
Dimensional Space ◽  
Principal Component ◽  
Component Analysis ◽  
Large Datasets ◽  
Stochastic Gradient Descent ◽  
High Dimensional ◽  
Excellent Method ◽  
Blue Cell

In this paper, a new hybridization of supervised principal component analysis (SPCA) and stochastic gradient descent techniques is proposed, and called as SGD-SPCA, for real large datasets that have a small number of samples in high dimensional space. SGD-SPCA is proposed to become an important tool that can be used to diagnose and treat cancer accurately. When we have large datasets that require many parameters, SGD-SPCA is an excellent method, and it can easily update the parameters when a new observation shows up. Two cancer datasets are used, the first is for Leukemia and the second is for small round blue cell tumors. Also, simulation datasets are used to compare principal component analysis (PCA), SPCA, and SGD-SPCA. The results show that SGD-SPCA is more efficient than other existing methods.

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