The Experts below are selected from a list of 500316 Experts worldwide ranked by ideXlab platform
Markus Ringnér - One of the best experts on this subject based on the ideXlab platform.
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What is principal Component Analysis?
Nat Biotechnol, 2008Co-Authors: Markus RingnérAbstract:Principal Component Analysis is often incorporated into genome-wide expression studies, but what is it and how can it be used to explore high-dimensional data?
Zhihuan Song - One of the best experts on this subject based on the ideXlab platform.
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process monitoring based on independent Component Analysis principal Component Analysis ica pca and similarity factors
Industrial & Engineering Chemistry Research, 2007Co-Authors: Zhiqiang Ge, Zhihuan SongAbstract:Many of the current multivariate statistical process monitoring techniques (such as principal Component Analysis (PCA) or partial least squares (PLS)) do not utilize the non-Gaussian information of process data. This paper proposes a new monitoring method based on independent Component Analysis−principal Component Analysis (ICA−PCA). The Gaussian and non-Gaussian information can be extracted for fault detection and diagnosis. Moreover, a new mixed similarity factor is proposed. This similarity factor is used to identify the fault mode. Because of the non-orthogonal nature of the extracted independent Components, a “main angle” is proposed to calculate the ICA-based similarity factor. To handle the cases where two datasets have similar principal Components or independent Components but the numerical values of the process variables are different, two distance similarity factors are used for complement. A case study of the Tennessee Eastman (TE) benchmark process indicates that the proposed fault detection a...
Andrzej Skowron - One of the best experts on this subject based on the ideXlab platform.
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Trans. Rough Sets - Independent Component Analysis, Principal Component Analysis and Rough Sets in Face Recognition
Lecture Notes in Computer Science, 2020Co-Authors: Roman W. Swiniarski, Andrzej SkowronAbstract:The paper contains description of hybrid methods of face recognition which are based on independent Component Analysis, principal Component Analysis and rough set theory. The feature extraction and pattern forming from face images have been provided using Independent Component Analysis and Principal Component Analysis. The feature selection/reduction has been realized using the rough set technique. The face recognition system was designed as rough-sets rule based classifier.
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IPCV - Independent Component Analysis, Princpal Component Analysis and Rough Sets in Hybrid Mammogram Classification
2020Co-Authors: Roman W. Swiniarski, Joo Heon Shin, Andrzej SkowronAbstract:The paper describes the hybrid methods of mammogram recognition which are based on independent Component Analysis, principal Component Analysis and rough set theory.
Shinto Eguchi - One of the best experts on this subject based on the ideXlab platform.
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Robust kernel principal Component Analysis
Neural Computation, 2009Co-Authors: Su-yun Huang, Shinto EguchiAbstract:This letter discusses the robustness issue of kernel principal Component Analysis. A class of new robust procedures is proposed based on eigenvalue decomposition of weighted covariance. The proposed procedures will place less weight on deviant patterns and thus be more resistant to data contamination and model deviation. Theoretical influence functions are derived, and numerical examples are presented as well. Both theoretical and numerical results indicate that the proposed robust method outperforms the conventional approach in the sense of being less sensitive to outliers. Our robust method and results also apply to functional principal Component Analysis.
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Robust principal Component Analysis?
Neural computation, 2009Co-Authors: Su-yun Huang, Yi-ren Yeh, Shinto EguchiAbstract:This letter discusses the robustness issue of kernel principal Component Analysis. A class of new robust procedures is proposed based on eigenvalue decomposition of weighted covariance. The proposed procedures will place less weight on deviant patterns and thus be more resistant to data contamination and model deviation. Theoretical influence functions are derived, and numerical examples are presented as well. Both theoretical and numerical results indicate that the proposed robust method outperforms the conventional approach in the sense of being less sensitive to outliers. Our robust method and results also apply to functional principal Component Analysis.
Age K. Smilde - One of the best experts on this subject based on the ideXlab platform.
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Principal Component Analysis
Analytical Methods, 2014Co-Authors: Age K. SmildeAbstract:Principal Component Analysis is one of the most important and powerful methods in chemometrics as well as in a wealth of other areas. This paper provides a description of how to understand, use, and interpret principal Component Analysis. The paper focuses on the use of principal Component Analysis in typical chemometric areas but the results are generally applicable.
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ANOVA–principal Component Analysis and ANOVA–simultaneous Component Analysis: a comparison
Journal of Chemometrics, 2011Co-Authors: Gooitzen Zwanenburg, Johan A Westerhuis, Huub C. J. Hoefsloot, Jeroen J. Jansen, Age K. SmildeAbstract:ANOVA-simultaneous Component Analysis (ASCA) is a recently developed tool to analyze multivariate data. In this paper, we enhance the explorative capability of ASCA by introducing a projection of the observations on the principal Component subspace to visualize the variation among the measurements. We compare the significance of experimental effects for ASCA and ANOVA-principal Component Analysis (PCA), a similar tool to explore multivariate data, by using permutation tests. Furthermore, we quantify the quality of the loadings estimate obtained with ASCA and compare this with the loadings estimate obtained with ANOVA-PCA.
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ANOVA – principal Component Analysis and ANOVA – simultaneous Component Analysis : a comparison
Journal of Chemometrics, 2011Co-Authors: Gooitzen Zwanenburg, Johan A Westerhuis, Huub C. J. Hoefsloot, Jeroen J. Jansen, Age K. SmildeAbstract:This work presents an enhancement of ANOVA simultaneous Component Analysis by projecting the observations onto the principal Component subspace, thus allowing the visualization of the variation of the measurements. Furthermore, using a synthetic data set, a comparison is made between the significance of experimental effects and the quality of estimated loadings for ANOVA-simultaneous Component Analysis and ANOVA-principal Component Analysis. ANOVA-simultaneous Component Analysis (ASCA) is a recently developed tool to analyze multivariate data. In this paper, we enhance the explorative capability of ASCA by introducing a projection of the observations on the principal Component subspace to visualize the variation among the measurements. We compare the significance of experimental effects for ASCA and ANOVA-principal Component Analysis (PCA), a similar tool to explore multivariate data, by using permutation tests. Furthermore, we quantify the quality of the loadings estimate obtained with ASCA and compare this with the loadings estimate obtained with ANOVA-PCA
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Grey Component Analysis
Journal of Chemometrics, 2007Co-Authors: Johan A Westerhuis, Huub C. J. Hoefsloot, Eduard P. P. A. Derks, Age K. SmildeAbstract:The interpretation of principal Component Analysis (PCA) models of complex biological or chemical data can be cumbersome because in PCA the decomposition is performed without any knowledge of the system at hand. Prior information of the system is not used to improve the interpretation. In this paper we introduce Grey Component Analysis (GCA) as a new explorative data Analysis method that uses the available prior information. GCA uses a soft penalty approach to gently push the decomposition into the direction of the prior information. The grey Components are therefore partly data driven and partly driven by the prior information. GCA works in a confirmatory mode to analyze the validity of the prior information and in an exploratory mode in which new phenomena can be studied in detail. To show the wide applicability of GCA, applications within spectroscopy and gene expression are presented. Many diagnostic properties of GCA are introduced and examples of erroneous parts within the prior information are indicated. Copyright © 2007 John Wiley & Sons, Ltd.
