Steganalysis Using High-Dimensional Features Derived from Co-occurrence Matrix and Class-Wise Non-Principal Components Analysis (CNPCA)

2006 ◽  
pp. 49-60 ◽  
Author(s):  
Guorong Xuan ◽  
Yun Q. Shi ◽  
Cong Huang ◽  
Dongdong Fu ◽  
Xiuming Zhu ◽  
...  
Keyword(s):  
Principal Components Analysis ◽  
Principal Components ◽  
High Dimensional ◽  
Components Analysis ◽  
Occurrence Matrix

scholarly journals Spurious Factor Analysis

Econometrica ◽  
2021 ◽  
Vol 89 (2) ◽  
pp. 591-614
Author(s):  
Alexei Onatski ◽  
Chen Wang
Keyword(s):  
Factor Analysis ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Unit Root ◽  
Information Criteria ◽  
High Dimensional ◽  
Spurious Regression ◽  
Nonstationary Data ◽  
Components Analysis ◽  
Data Variation

This paper draws parallels between the principal components analysis of factorless high‐dimensional nonstationary data and the classical spurious regression. We show that a few of the principal components of such data absorb nearly all the data variation. The corresponding scree plot suggests that the data contain a few factors, which is corroborated by the standard panel information criteria. Furthermore, the Dickey–Fuller tests of the unit root hypothesis applied to the estimated “idiosyncratic terms” often reject, creating an impression that a few factors are responsible for most of the nonstationarity in the data. We warn empirical researchers of these peculiar effects and suggest to always compare the analysis in levels with that in differences.

Diagnoses Generated by Numerical Taxonomic Methods Applied to Standard Blood Variables

1980 ◽  
Vol 19 (04) ◽  
pp. 205-209
Author(s):  
L. A. Abbott ◽  
J. B. Mitton
Keyword(s):  
Liver Cirrhosis ◽  
Renal Disease ◽  
Principal Components Analysis ◽  
Discriminant Function ◽  
Principal Components ◽  
Discriminant Function Analysis ◽  
Chronic Renal Disease ◽  
Function Analysis ◽  
Infectious Hepatitis ◽  
Components Analysis

Data taken from the blood of 262 patients diagnosed for malabsorption, elective cholecystectomy, acute cholecystitis, infectious hepatitis, liver cirrhosis, or chronic renal disease were analyzed with three numerical taxonomy (NT) methods : cluster analysis, principal components analysis, and discriminant function analysis. Principal components analysis revealed discrete clusters of patients suffering from chronic renal disease, liver cirrhosis, and infectious hepatitis, which could be displayed by NT clustering as well as by plotting, but other disease groups were poorly defined. Sharper resolution of the same disease groups was attained by discriminant function analysis.

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