Search results for "principal component analysi"
showing 10 items of 489 documents
Local vs global motions in protein folding
2013
It is of interest to know whether local fluctuations in a polypeptide chain play any role in the mechanism by which the chain folds to the native structure of a protein. This question is addressed by analyzing folding and non-folding trajectories of a protein; as an example, the analysis is applied to the 37-residue triple β-strand WW domain from the Formin binding protein 28 (FBP28) (PDB ID: 1E0L). Molecular dynamics (MD) trajectories were generated with the coarse-grained united-residue force field, and one- and two-dimensional free-energy landscapes (FELs) along the backbone virtual-bond angle θ and backbone virtual-bond-dihedral angle γ of each residue, and principal components, respect…
Authentication of the protected designation of origin horchata de Valencia through the chemometric treatment of mineral content
2010
Spanish horchata de chufa samples were authenticated based on the determination of mineral elements by inductively coupled plasma atomic emission spectrometry (ICP-OES) combined with different chemometric methods. The ability of multivariate analysis, such as principle components analysis (PCA), classification and regression trees (CARTs) and discriminant analysis (DA) were evaluated in order to achieve a correct sample classification. It was possible to clearly differentiate homemade and long-life commercial samples by all three methods and CART and DA provided an excellent tool to establish the growth origin of the tiger nuts. CART analysis employed the concentration of Mg to discriminate…
Classification of persimmon fruit origin by near infrared spectrometry and least squares-support vector machines
2014
Abstract The main objective of this work has been the authentication by Fourier transform near infrared (FT-NIR) spectrometry of the origin of persimmon fruits cultivated in different regions of Spain. In order to achieve this goal, 166 persimmon samples from 7 different regions of Spain were analyzed by FT-NIR spectrometry. By splitting the spectral data in training and independent test sets, a classification model was built using least squares support vector machines chemometric technique. Orthogonal signal correction and principal component analysis were performed prior to conduct the classification strategy. The verified model was applied for the prediction of the origin of 50 samples f…
Alternative method for binary shape alignment of non-symmetrical shapes based on minimal enclosing box
2012
Proposed is a novel method based on the minimal enclosing box (MEB) to determine the canonical orientation associated with a three-dimensional binary shape. It is suggested that, when the shape has no clear distinctive features and two or more of the eigenvalues are similar, this method is more suitable than the commonly used method based on principal component analysis (PCA). An experiment is performed with shapes of human livers by measuring the degree on which a prototypical image (atlas) matches to a new shape after alignment by PCA, minimal area projection (MAP), and MEB showing that in this case MEB outperforms the usual PCA-based alignment method and also the MAP method.
Visible-NIR reflectance spectroscopy and manifold learning methods applied to the detection of fungal infections on citrus fruit
2015
Abstract The development of systems for automatically detecting decay in citrus fruit during quality control is still a challenge for the citrus industry. The feasibility of reflectance spectroscopy in the visible and near infrared (NIR) regions was evaluated for the automatic detection of the early symptoms of decay caused by Penicillium digitatum fungus in citrus fruit. Reflectance spectra of sound and decaying surface parts of mandarins cv. ‘Clemenvilla’ were acquired in two different spectral regions, from 650 nm to 1050 nm (visible–NIR) and from 1000 nm to 1700 nm (NIR), pointing to significant differences in spectra between sound and decaying skin for both spectral ranges. Three diffe…
Nonlinear data description with Principal Polynomial Analysis
2012
Principal Component Analysis (PCA) has been widely used for manifold description and dimensionality reduction. Performance of PCA is however hampered when data exhibits nonlinear feature relations. In this work, we propose a new framework for manifold learning based on the use of a sequence of Principal Polynomials that capture the eventually nonlinear nature of the data. The proposed Principal Polynomial Analysis (PPA) is shown to generalize PCA. Unlike recently proposed nonlinear methods (e.g. spectral/kernel methods and projection pursuit techniques, neural networks), PPA features are easily interpretable and the method leads to a fully invertible transform, which is a desirable property…
Spectral clustering with the probabilistic cluster kernel
2015
Abstract This letter introduces a probabilistic cluster kernel for data clustering. The proposed kernel is computed with the composition of dot products between the posterior probabilities obtained via GMM clustering. The kernel is directly learned from the data, is parameter-free, and captures the data manifold structure at different scales. The projections in the kernel space induced by this kernel are useful for general feature extraction purposes and are here exploited in spectral clustering with the canonical k-means. The kernel structure, informative content and optimality are studied. Analysis and performance are illustrated in several real datasets.
Feature extraction from remote sensing data using Kernel Orthonormalized PLS
2007
This paper presents the study of a sparse kernel-based method for non-linear feature extraction in the context of remote sensing classification and regression problems. The so-called kernel orthonormalized PLS algorithm with reduced complexity (rKOPLS) has two core parts: (i) a kernel version of OPLS (called KOPLS), and (ii) a sparse (reduced) approximation for large scale data sets, which ultimately leads to rKOPLS. The method demonstrates good capabilities in terms of expressive power of the extracted features and scalability.
Robustness of texture parameters for color texture analysis
2006
This article proposes to deal with noisy and variable size color textures. It also proposes to deal with quantization methods and to see how such methods change final results. The method we use to analyze the robustness of the textures consists of an auto-classification of modified textures. Texture parameters are computed for a set of original texture samples and stored into a database. Such a database is created for each quantization method. Textures from the set of original samples are then modified, eventually quantized and classified according to classes determined from a precomputed database. A classification is considered incorrect if the original texture is not retrieved. This metho…
Semisupervised Kernel Feature Extraction for Remote Sensing Image Analysis
2014
This paper presents a novel semisupervised kernel partial least squares (KPLS) algorithm for nonlinear feature extraction to tackle both land-cover classification and biophysical parameter retrieval problems. The proposed method finds projections of the original input data that align with the target variable (labels) and incorporates the wealth of unlabeled information to deal with low-sized or underrepresented data sets. The method relies on combining two kernel functions: the standard radial-basis-function kernel based on labeled information and a generative, i.e., probabilistic, kernel directly learned by clustering the data many times and at different scales across the data manifold. Th…