Search results for "Principal component analysis"

Basic Chemometric Tools

2013

Abstract The authentication of protected designation of origin and other protected geographical indications for foods involves the need for a deep knowledge of these kinds of samples and the correct identification of appropriate markers that are suitable to be used for authentication purposes. For this, significance tests must be developed and applied to provide evidence in a fast and accurate way; from this, it seems clear that advances in analytical tools, to obtain data regarding food chemical composition, and chemometric data treatments must be continued to provide to the users powerful identification methodologies. In this sense, the objective must be to differentiate between foods pro…

Characterization and source identification of polycyclic aromatic hydrocarbons (PAHs) in river bank soils.

2008

Elevated PAH concentrations were detected in bank soils along the Mosel and Saar Rivers in Germany. Information on the identification of PAH sources in this area however remains unclear. This study was able to characterize the PAH sources by application of several approaches, including consideration of the distribution patterns of 45 PAHs (including 16 EPA PAHs and some alkyl PAHs), specific PAH ratios, distribution patterns of n-alkanes and principal component analysis (PCA). In addition, the efficiency of the tested approaches was assessed. The results from the application of the various source identification methods showed that pyrogenic PAHs dominate soil samples collected upstream of t…

Comparative study of multi-2D, Full 3D and hybrid strategies for multi/hyperspectral image compression

2009

In this paper, we investigate appropriate strategies for multi/hyperspectral image compression. In particular, we compare the classic multi-2D compression strategy and two different implementations of 3D strategies (Full 3D and hybrid). All strategies are combined with a PCA decorrelation stage to optimize performance. For multi-2D and hybrid strategies, we propose a weighted version of PCA. Finally, for consistent evaluation, we propose a larger comparison framework than the conventionally used PSNR. The results are significant and show the weaknesses and strengths of each strategy.

Image enhancement by region detection on CFA data images

2007

An overview of incremental feature extraction methods based on linear subspaces

2018

Abstract With the massive explosion of machine learning in our day-to-day life, incremental and adaptive learning has become a major topic, crucial to keep up-to-date and improve classification models and their corresponding feature extraction processes. This paper presents a categorized overview of incremental feature extraction based on linear subspace methods which aim at incorporating new information to the already acquired knowledge without accessing previous data. Specifically, this paper focuses on those linear dimensionality reduction methods with orthogonal matrix constraints based on global loss function, due to the extensive use of their batch approaches versus other linear alter…

Learning with the kernel signal to noise ratio

2012

This paper presents the application of the kernel signal to noise ratio (KSNR) in the context of feature extraction to general machine learning and signal processing domains. The proposed approach maximizes the signal variance while minimizes the estimated noise variance in a reproducing kernel Hilbert space (RKHS). The KSNR can be used in any kernel method to deal with correlated (possibly non-Gaussian) noise. We illustrate the method in nonlinear regression examples, dependence estimation and causal inference, nonlinear channel equalization, and nonlinear feature extraction from high-dimensional satellite images. Results show that the proposed KSNR yields more fitted solutions and extract…

Explicit signal to noise ratio in reproducing kernel Hilbert spaces

2011

This paper introduces a nonlinear feature extraction method based on kernels for remote sensing data analysis. The proposed approach is based on the minimum noise fraction (MNF) transform, which maximizes the signal variance while also minimizing the estimated noise variance. We here propose an alternative kernel MNF (KMNF) in which the noise is explicitly estimated in the reproducing kernel Hilbert space. This enables KMNF dealing with non-linear relations between the noise and the signal features jointly. Results show that the proposed KMNF provides the most noise-free features when confronted with PCA, MNF, KPCA, and the previous version of KMNF. Extracted features with the explicit KMNF…

Cross-Talk Correction Method for Knee Kinematics in Gait Analysis Using Principal Component Analysis (PCA): A New Proposal

2014

International audience; Background: In 3D gait analysis, the knee joint is usually described by the Eulerian way. It consists in breaking down the motion between the articulating bones of the knee into three rotations around three axes: flexion/extension, abduction/adduction and internal/external rotation. However, the definition of these axes is prone to error, such as the "cross-talk'' effect, due to difficult positioning of anatomical landmarks. This paper proposes a correction method, principal component analysis (PCA), based on an objective kinematic criterion for standardization, in order to improve knee joint kinematic analysis. Methods: The method was applied to the 3D gait data of …

ChemInform Abstract: Chemometrics: An Important Tool for the Modern Chemist, an Example from Wood-Processing Chemistry.

2010

This study briefly outlines the idea of principal component analysis and cross-correlation calculations (applied chemometrics) and presents an illustrative example from wood-processing chemistry. The applicability of chemometric data analysis was demonstrated by investigating the various structural changes that take place in dissolved and degraded lignin ("kraft lignin") during laboratory-scale kraft pulping of Scots pine (Pinus sylvestris) and silver birch (Betula pendula). The structural data (31P NMR and size exclusion chromatographic data) on kraft lignin were further processed by chemometric multivariate techniques (PCA and 2DCC), confirming, for example, that the cleavage of beta-aryl…

Chemometrics:  An Important Tool for the Modern Chemist, an Example from Wood-Processing Chemistry

2000

This study briefly outlines the idea of principal component analysis and cross-correlation calculations (applied chemometrics) and presents an illustrative example from wood-processing chemistry. The applicability of chemometric data analysis was demonstrated by investigating the various structural changes that take place in dissolved and degraded lignin ("kraft lignin") during laboratory-scale kraft pulping of Scots pine (Pinus sylvestris) and silver birch (Betula pendula). The structural data (31P NMR and size exclusion chromatographic data) on kraft lignin were further processed by chemometric multivariate techniques (PCA and 2DCC), confirming, for example, that the cleavage of beta-aryl…