scholarly journals Profile Analysis in High Dimensions

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
Cigdem Cengiz ◽  
Dietrich von Rosen ◽  
Martin Singull
Keyword(s):  
Dimension Reduction ◽  
Profile Analysis ◽  
High Dimensional Data ◽  
High Dimensional ◽  
High Dimensions ◽  
Null Distributions

AbstractThe three tests in profile analysis: test of parallelism, test of level and test of flatness are modified so that high-dimensional data can be analysed. Using specific scores, dimension reduction is performed and the exact null distributions are derived for the three hypotheses.

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 Canonical correlation analysis in high dimensions with structured regularization

2021 ◽  
pp. 1471082X2110410
Author(s):  
Elena Tuzhilina ◽  
Leonardo Tozzi ◽  
Trevor Hastie
Keyword(s):  
Data Structure ◽  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Multivariate Data ◽  
High Dimensional Data ◽  
High Dimensional ◽  
High Dimensions ◽  
Structured Regularization ◽  
Data Matrices

Canonical correlation analysis (CCA) is a technique for measuring the association between two multivariate data matrices. A regularized modification of canonical correlation analysis (RCCA) which imposes an [Formula: see text] penalty on the CCA coefficients is widely used in applications with high-dimensional data. One limitation of such regularization is that it ignores any data structure, treating all the features equally, which can be ill-suited for some applications. In this article we introduce several approaches to regularizing CCA that take the underlying data structure into account. In particular, the proposed group regularized canonical correlation analysis (GRCCA) is useful when the variables are correlated in groups. We illustrate some computational strategies to avoid excessive computations with regularized CCA in high dimensions. We demonstrate the application of these methods in our motivating application from neuroscience, as well as in a small simulation example.

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
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