Search results for "Component analysis"
showing 10 items of 562 documents
Metabolomics analysis and biological investigation of three Malvaceae plants
2019
Introduction: Metabolomics is a fast growing technology that has effectively contributed to many plant-related sciences and drug discovery. Objective: To use the non-targeted metabolomics approach to investigate the chemical profiles of three Malvaceae plants, namely Hibiscus mutabilis L. (Changing rose), H. schizopetalus (Dyer) Hook.f. (Coral Hibiscus), and Malvaviscus arboreus Cav. (Sleeping Hibiscus), along with evaluating their antioxidant and anti-infective potential. Methodology: Metabolic profiling was carried out using liquid chromatography coupled with high-resolution electrospray ionisation mass spectrometry (LC-HR-ESI-MS) for dereplication purposes. The chemical composition of th…
Rapid determination of baicalin and total baicalein content in Scutellariae radix by ATR-IR and NIR spectroscopy
2013
In this study methods for the quantification of baicalin and total baicalein in Scutellariae radix with near infrared (NIR) spectroscopy and attenuated-total-reflectance mid-infrared (ATR-IR) spectroscopy in hyphenation with multivariate analysis were developed and compared. The reference analysis was performed by high performance liquid chromatography coupled to diode array detection (HPLC-DAD). Different pretreatments like standard normal variate (SNV), multiplicative scatter correction (MSC), first and second derivative Savitzky-Golay were applied on the spectra to optimize the calibrations. A principal component analysis was performed with both spectroscopic methods to distinguish wild …
Coupling of Action-Perception Brain Networks during Musical Pulse Processing: Evidence from Region-of-Interest-Based Independent Component Analysis
2017
Our sense of rhythm relies on orchestrated activity of several cerebral and cerebellar structures. Although functional connectivity studies have advanced our understanding of rhythm perception, this phenomenon has not been sufficiently studied as a function of musical training and beyond the General Linear Model (GLM) approach. Here, we studied pulse clarity processing during naturalistic music listening using a data-driven approach (independent component analysis; ICA). Participants’ (18 musicians and 18 controls) functional magnetic resonance imaging (fMRI) responses were acquired while listening to music. A targeted region of interest (ROI) related to pulse clarity processing was defined…
Growth in Average Firm Size of U.S. Industrial Portfolios and the Cross-Section of Expected Returns
2018
This paper shows that growth in average firm size in U.S. industrial portfolios predicts future growth in average firm size. Moreover, the payoffs of industrial portfolios sorted by growth in average firm size in the previous period increase linearly as we move from lowest to highest growth in average firm size. The spread between highest and lowest growth in average firm size is economically large and cannot be explained by exposures to standard risk factors or the asset growth effect (Cooper, Gulen, and Schill, 2008). Principal component analysis reveals that this growth in average firm size effect is linked to the first principal component. Moreover, stochastic discount factor model anal…
Unfolding dynamics of small peptides biased by constant mechanical forces
2018
We show how multi-ensemble Markov state models can be combined with constant-force equilibrium simulations. Besides obtaining the unfolding/folding rates, Markov state models allow gaining detailed insights into the folding dynamics and pathways through identifying folding intermediates and misfolded structures. For two specific peptides, we demonstrate that the end-to-end distance is an insufficient reaction coordinate. This problem is alleviated through constructing models with multiple collective variables, for which we employ the time-lagged independent component analysis requiring only minimal prior knowledge. Our results show that combining Markov state models with constant-force simu…
Nonlinear Complex PCA for spatio-temporal analysis of global soil moisture
2020
Soil moisture (SM) is a key state variable of the hydrological cycle, needed to monitor the effects of a changing climate on natural resources. Soil moisture is highly variable in space and time, presenting seasonalities, anomalies and long-term trends, but also, and important nonlinear behaviours. Here, we introduce a novel fast and nonlinear complex PCA method to analyze the spatio-temporal patterns of the Earth's surface SM. We use global SM estimates acquired during the period 2010-2017 by ESA's SMOS mission. Our approach unveils both time and space modes, trends and periodicities unlike standard PCA decompositions. Results show the distribution of the total SM variance among its differ…
Using Chemical Structural Indicators for Periodic Classification of Local Anaesthetics
2011
Algorithms for classification and taxonomy based on criteria as information entropy and its production are proposed. Some local anaesthetics, currently in use, are classified using five characteristic chemical properties of different portions of their molecules. Many classification algorithms are based on information entropy. When applying the procedures to sets of moderate size, an excessive number of results appear compatible with data and the number suffers a combinatorial explosion. However, after the equipartition conjecture one has a selection criterion between different variants resulting from classification between hierarchical trees. Information entropy and principal component anal…
Independent component analysis based on symmetrised scatter matrices
2007
A new method for separating the mixtures of independent sources has been proposed recently in [Oja et al. (2006). Scatter matrices and independent component analysis. Austrian J. Statist., to appear]. This method is based on two scatter matrices with the so-called independence property. The corresponding method is now further examined. Simple simulation studies are used to compare the performance of so-called symmetrised scatter matrices in solving the independence component analysis problem. The results are also compared with the classical FastICA method. Finally, the theory is illustrated by some examples. peerReviewed
On Independent Component Analysis with Stochastic Volatility Models
2017
Consider a multivariate time series where each component series is assumed to be a linear mixture of latent mutually independent stationary time series. Classical independent component analysis (ICA) tools, such as fastICA, are often used to extract latent series, but they don't utilize any information on temporal dependence. Also financial time series often have periods of low and high volatility. In such settings second order source separation methods, such as SOBI, fail. We review here some classical methods used for time series with stochastic volatility, and suggest modifications of them by proposing a family of vSOBI estimators. These estimators use different nonlinearity functions to…
Online Principal Component Analysis in High Dimension: Which Algorithm to Choose?
2017
Summary Principal component analysis (PCA) is a method of choice for dimension reduction. In the current context of data explosion, online techniques that do not require storing all data in memory are indispensable to perform the PCA of streaming data and/or massive data. Despite the wide availability of recursive algorithms that can efficiently update the PCA when new data are observed, the literature offers little guidance on how to select a suitable algorithm for a given application. This paper reviews the main approaches to online PCA, namely, perturbation techniques, incremental methods and stochastic optimisation, and compares the most widely employed techniques in terms statistical a…