Search results for "principal component analysi"
showing 10 items of 489 documents
A family of kernel anomaly change detectors
2014
This paper introduces the nonlinear extension of the anomaly change detection algorithms in [1] based on the theory of reproducing kernels. The presented methods generalize their linear counterparts, under both the Gaussian and elliptically-contoured assumptions, and produce both improved detection accuracies and reduced false alarm rates. We study the Gaussianity of the data in Hilbert spaces with kernel dependence estimates, provide low-rank kernel versions to cope with the high computational cost of the methods, and give prescriptions about the selection of the kernel functions and their parameters. We illustrate the performance of the introduced kernel methods in both pervasive and anom…
Semi-Supervised Remote Sensing Image Classification based on Clustering and the Mean Map Kernel
2008
This paper presents a semi-supervised classifier based on the combination of the expectation-maximization (EM) algorithm for Gaussian mixture models (GMM) and the mean map kernel. The proposed method uses the most reliable samples in terms of maximum likelihood to compute a kernel function that accurately reflects the similarity between clusters in the kernel space. The proposed method improves classification accuracy in situations where the available labeled information does not properly describe the classes in the test image.
An Efficient Method for the Visualization of Spectral Images Based on a Perception-Oriented Spectrum Segmentation
2010
We propose a new method for the visualization of spectral images. It involves a perception-based spectrum segmentation using an adaptable thresholding of the stretched CIE standard observer colormatching functions. This allows for an underlying removal of irrelevant channels, and, consequently, an alleviation of the computational burden of further processings. Principal Components Analysis is then used in each of the three segments to extract the Red, Green and Blue primaries for final visualization. A comparison framework using two different datasets shows the efficiency of the proposed method.
Natural oxygenation of Champagne wine during ageing on lees: A metabolomics picture of hormesis
2016
International audience; The oxygenation of Champagne wine after 4 and 6 years of aging on lees in bottle was investigated by FTICR-MS and UPLC-Q-TOF-MS. Three levels of permeability were considered for the stoppers, ranging from 0.2 to 1.8 mg/L/year of oxygen transfer rate. Our results confirmed a good repeatability of ultrahigh resolution FTICR-MS, both in terms of m/z and coefficient of variation of peak intensities among biological replicates. Vintages appeared to be the most discriminated features, and metabolite annotations suggested that the oldest wines (2006) were characterized by a higher sensitivity towards oxygenation. Within each vintage, the oxygenation mechanisms appeared to b…
Polychlorinated Biphenyls in Sediments from Sicilian Coastal Area (Scoglitti) using Automated Soxhlet, GC-MS, and Principal Component Analysis
2014
A methodology for the PAHs and PCBs congener determination in sediment samples has been revised. We determined the distributions of PAHs and PCBs in the superficial sediments of the Scoglitti (Italy) coastal area to provide data for comparison with other marine systems and to hypothesize the sources. Extraction yield, for PCB, was never less than 60% in most cases, while for PAHs, utilizing perdeuterated surrogate standard (benz[a]anthracene-d12 and anthracene-d10) was never less than 72%. The total concentration of the 16 PAHs investigated, expressed as the sum of concentrations, PAHs, varied from 1–5087 μg/kg of dry matrix, while the PCBs ranged from detection limit to 36 μg/kg of dry mat…
Applying fully tensorial ICA to fMRI data
2016
There are two aspects in functional magnetic resonance imaging (fMRI) data that make them awkward to analyse with traditional multivariate methods - high order and high dimension. The first of these refers to the tensorial nature of observations as array-valued elements instead of vectors. Although this can be circumvented by vectorizing the array, doing so simultaneously loses all the structural information in the original observations. The second aspect refers to the high dimensionality along each dimension making the concept of dimension reduction a valuable tool in the processing of fMRI data. Different methods of tensor dimension reduction are currently gaining popUlarity in literature…
Identifying critical factors for implementing good agricultural practice
2009
En este artículo se presenta la identificación de los factores críticos (FC) que afectan la implantación de un programa de buenas prácticas agrícolas (BPA) en productores de café y frutas del departamento del Huila, en Colombia, mediante la realización de un análisis factorial exploratorio utilizando como método de factorización el análisis de componentes principales (ACP); las matrices de datos se construyeron con los resultados de la aplicación de sendos instrumentos con estructura definida en las dos poblaciones objeto de estudio, el instrumento Starbucks C.A.F.E. Practices -para pequeños caficultores en el caso de los productores de café- y EUREPGAP V2.1 Oct.2004 - Checklist-listado de …
A data-driven surrogate-assisted evolutionary algorithm applied to a many-objective blast furnace optimization problem
2017
A new data-driven reference vector-guided evolutionary algorithm has been successfully implemented to construct surrogate models for various objectives pertinent to an industrial blast furnace. A total of eight objectives have been modeled using the operational data of the furnace using 12 process variables identified through a principal component analysis and optimized simultaneously. The capability of this algorithm to handle a large number of objectives, which has been lacking earlier, results in a more efficient setting of the operational parameters of the furnace, leading to a precisely optimized hot metal production process. peerReviewed
Beyond Tandem Analysis: Joint Dimension Reduction and Clustering in R
2019
We present the R package clustrd which implements a class of methods that combine dimension reduction and clustering of continuous or categorical data. In particular, for continuous data, the package contains implementations of factorial K-means and reduced K-means; both methods combine principal component analysis with K-means clustering. For categorical data, the package provides MCA K-means, i-FCB and cluster correspondence analysis, which combine multiple correspondence analysis with K-means. Two examples on real data sets are provided to illustrate the usage of the main functions.
Automated detection and localization system of myocardial infarction in single-beat ECG using Dual-Q TQWT and wavelet packet tensor decomposition
2020
Background and objective. It is challenging to conduct real-time identification of myocardial infarction (MI) due to artifact corruption and high dimensionality of multi-lead electrocardiogram (ECG). In the present study, we proposed an automated single-beat MI detection and localization system using dual-Q tunable Q-factor wavelet transformation (Dual-Q TQWT) denoising algorithm. Methods. After denoising and segmentation of ECG, a fourth-order wavelet tensor (leads × subbands × samples × beats) was constructed based on thediscretewavelet packet transform (DWPT), to represent the features considering the information of inter-beat, intra-beat, inter-frequency, and inter-lead. To reduce the t…