scholarly journals Fortify particle swam optimizer (PSO) with principal components analysis: A case study in improving bound-handling for optimizing high-dimensional and complex problems

2011 ◽  
Author(s):  
Wei Chu ◽  
Xiaogang Gao ◽  
Soroosh Sorooshian
Keyword(s):  
Principal Components Analysis ◽  
Principal Components ◽  
High Dimensional ◽  
Complex Problems ◽  
Components Analysis

scholarly journals Raman investigation of corrosion products on Roman copper-based artefacts

ACTA IMEKO ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 129
Author(s):  
Leila Es Sebar ◽  
Leonardo Iannucci ◽  
Yuval Goren ◽  
Peter Fabian ◽  
Emma Angelini ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Roman Period ◽  
Corrosion Products ◽  
Beer Sheva ◽  
Chemometric Technique ◽  
Components Analysis ◽  
Main Components

<p class="Abstract">This paper illustrates a case study related to the characterisation of corrosion products present on recently excavated artefacts. The archaeological findings, from the Rakafot 54 site (Beer-Sheva, Israel), consist of 23 coins and a pendant, all dating back to the Roman period. Raman spectroscopy was used to identify the corrosion products that compose the patina covering the objects. To facilitate and support their identification, spectra were then processed using principal components analysis. This chemometric technique allowed the identification of two main compounds, classified as atacamite and clinoatacamite, which formed the main components of the patinas. The results of this investigation can help in assessing the conservation state of artefacts and defining the correct restoration strategy.</p>

Using multivariate factorial kriging for multiscale ordination: a case study

2005 ◽  
Vol 35 (12) ◽  
pp. 2860-2874 ◽  
Author(s):  
Nikos Nanos ◽  
Fernando Pardo ◽  
Jesus Alonso Nager ◽  
José Alberto Pardos ◽  
Luis Gil
Keyword(s):  
Principal Components Analysis ◽  
Principal Components ◽  
Multiple Scale ◽  
Analysis Data ◽  
Vegetation Pattern ◽  
Soil Conditions ◽  
Linear Model Of Coregionalization ◽  
Factorial Kriging ◽  
Components Analysis

Vegetation ordination is usually based on classical data reduction techniques such as principal components analysis, correspondence analysis, or multidimensional scaling. The usual methods do not account for multiscale correlations among species. In this paper, we use a geostatistical method, known as multivariate factorial kriging, for studying multiple-scale correlations. The case study was carried out in a mixed broadleaf forest of central Spain. Six tree species were included in the analysis. Data analysis included (i) experimental variogram calculation and modeling with the use of the linear model of coregionalization, (ii) principal components analysis, and (iii) cokriging. The results indicate that correlations among species are different depending on the spatial scale. We conclude that competition for light is the main factor controlling the spatial distribution of species at the plot-level scale of variation. At larger scales of variation, soil conditions and (or) human intervention are the key factors in determining the observed vegetation pattern. Based on the factor scores for the largest scale of variation, we conducted a cluster analysis to identify plots with similar characteristics. The resulting clusters have the remarkable property of being spatially continuous.

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.

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