scholarly journals For visualization-based analysis tools in knowledge discovery process: A multilayer perceptron versus principal components analysis: A comparative study

1998 ◽  
pp. 28-37
Author(s):  
Xavier Polanco ◽  
Claire François ◽  
Mohamed Aly Ould Louly
Keyword(s):  
Comparative Study ◽  
Knowledge Discovery ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Multilayer Perceptron ◽  
Discovery Process ◽  
Analysis Tools ◽  
Components Analysis

A Comparative Study of the Dimensionality of the Self-Concealment Scale Using Principal Components Analysis and Mokken Scale Analysis

2008 ◽  
Vol 90 (4) ◽  
pp. 323-334 ◽  
Author(s):  
Andreas A. J. Wismeijer ◽  
Klaas Sijtsma ◽  
Marcel A. L. M. van Assen ◽  
Ad J. J. M. Vingerhoets
Keyword(s):  
Comparative Study ◽  
Principal Components Analysis ◽  
Principal Components ◽  
The Self ◽  
Scale Analysis ◽  
Mokken Scale Analysis ◽  
Components Analysis

Beyond principal components: a critical comparison of factor analysis methods for subspace modelling in chemistry

2021 ◽  
Vol 13 (37) ◽  
pp. 4188-4219
Author(s):  
Peter D. Wentzell ◽  
Cannon Giglio ◽  
Mohsen Kompany-Zareh
Keyword(s):  
Analytical Chemistry ◽  
Factor Analysis ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Critical Comparison ◽  
Analysis Methods ◽  
Analysis Tools ◽  
Components Analysis

Principal components analysis (PCA) is widely used in analytical chemistry, but is only one type of broader range of factor analysis tools that are described in this article.

scholarly journals Low-loss image compression techniques for cutting tool images: a comparative study of compression quality measures DOI: 10.5585/exacta.v8i2.2000

Exacta ◽  
2010 ◽  
Vol 8 (2) ◽  
pp. 225-235
Author(s):  
Fábio Henrique Pereira ◽  
Elesandro Antonio Baptista ◽  
Nivaldo Lemos Coppini ◽  
Rafael Do Espírito-Santo ◽  
Ademir João de Oliveira
Keyword(s):  
Image Compression ◽  
Comparative Study ◽  
Principal Components Analysis ◽  
Principal Components ◽  
High Performance ◽  
Cutting Tool ◽  
Low Loss ◽  
Compression Process ◽  
Lifting Technique ◽  
Components Analysis

This work accomplishes a comparative study between two distinct image compression techniques, namely the Lifting technique and the Principal Components Analysis (PCA), in order to determine what of these two approaches is more appropriate for cutting tool wear images analysis. Lifting and Principal Components Analysis were applied in original images of a cutting tool for producing a low resolution version, while keeping the more important details of the image. The low-loss image compression quality provided by these techniques was expressed in terms of the compression factor (ρ), the Mean Square Error (MSE) and the Peak Signal-to-Noise Rate (PSNR) provided by the image compression process. The tests were accomplished using the high-performance language for technical computing MATLAB®, and the results shown that the PCA technique presented the best values of PSNR with low compression rates. However, with high values of compression rates the lifting technique gave the highest PSNR.

scholarly journals Low-loss image compression techniques for cutting tool images: a comparative study of compression quality measures

Exacta ◽  
2010 ◽  
Vol 8 (2) ◽  
pp. 225-235
Author(s):  
Fábio Henrique Pereira ◽  
Elesandro Antonio Baptista ◽  
Nivaldo Lemos Coppini ◽  
Rafael Do Espírito-Santo ◽  
Ademir João de Oliveira
Keyword(s):  
Image Compression ◽  
Comparative Study ◽  
Principal Components Analysis ◽  
Principal Components ◽  
High Performance ◽  
Cutting Tool ◽  
Low Loss ◽  
Compression Process ◽  
Lifting Technique ◽  
Components Analysis

This work accomplishes a comparative study between two distinct image compression techniques, namely the Lifting technique and the Principal Components Analysis (PCA), in order to determine what of these two approaches is more appropriate for cutting tool wear images analysis. Lifting and Principal Components Analysis were applied in original images of a cutting tool for producing a low resolution version, while keeping the more important details of the image. The low-loss image compression quality provided by these techniques was expressed in terms of the compression factor (ρ), the Mean Square Error (MSE) and the Peak Signal-to-Noise Rate (PSNR) provided by the image compression process. The tests were accomplished using the high-performance language for technical computing MATLAB®, and the results shown that the PCA technique presented the best values of PSNR with low compression rates. However, with high values of compression rates the lifting technique gave the highest PSNR.

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