Search results for "Principal component"
showing 10 items of 514 documents
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…
Chemometric investigation on structural changes in pine kraft lignin during pulping
2000
Abstract Various structural changes which take place in dissolved lignin during the laboratory-scale kraft pulping of Scots pine (Pinus sylvestris) were studied. Lignin samples were subjected to the alkaline cupric oxide oxidation and the analytical data further processed by various multivariate chemometric techniques (principal component analysis, PCA; principal component regression, PCR; and projection to latent structures, PLS). Several models applicable to the indirect measurement of common pine kraft pulp properties (i.e., total cooking yield, kappa number and ISO brightness) were produced.