Search results for " Principal Component Analysis"
showing 10 items of 71 documents
Functional principal component analysis as a new methodology for the analysis of the impact of two rehabilitation protocols in functional recovery af…
2014
[EN] Background: This study addressed the problem of evaluating the effectiveness of two protocols of physiotherapy for functional recovery after stroke. In particular, the study explored the use of Functional Principal Component Analysis (FPCA), a multivariate data analysis in order to assess and clarify the process of regaining independence after stroke. Methods: A randomized double-blind controlled trial was performed. Thirteen subjects with residual hemiparesis after a single stroke episode were measured in both in- and outpatient settings at a district hospital. All subjects were able to walk before suffering the stroke and were hemodynamically stable within the first week after stroke…
An Introduction to Kernel Methods
2009
Machine learning has experienced a great advance in the eighties and nineties due to the active research in artificial neural networks and adaptive systems. These tools have demonstrated good results in many real applications, since neither a priori knowledge about the distribution of the available data nor the relationships among the independent variables should be necessarily assumed. Overfitting due to reduced training data sets is controlled by means of a regularized functional which minimizes the complexity of the machine. Working with high dimensional input spaces is no longer a problem thanks to the use of kernel methods. Such methods also provide us with new ways to interpret the cl…
Signal-to-noise ratio in reproducing kernel Hilbert spaces
2018
This paper introduces the kernel signal-to-noise ratio (kSNR) for different machine learning and signal processing applications}. The kSNR seeks to maximize the signal variance while minimizing the estimated noise variance explicitly in a reproducing kernel Hilbert space (rkHs). The kSNR gives rise to considering complex signal-to-noise relations beyond additive noise models, and can be seen as a useful signal-to-noise regularizer for feature extraction and dimensionality reduction. We show that the kSNR generalizes kernel PCA (and other spectral dimensionality reduction methods), least squares SVM, and kernel ridge regression to deal with cases where signal and noise cannot be assumed inde…
Impact of Global Economic Crisis on the European Welfare States
2013
The global economic crisis and the subsequent weaker growth are putting under pressure welfare states in the EU. This paper aims at discussing the effects of the crisis at the social level and at identifying whether the classic European welfare state models (Nordic, Continental, Anglo-Saxon and Mediterranean) are still valid in today’s economy. An answer will be tried using the mathematical tool of principal components analysis. The results will be observed in graphs where the states taken into consideration respect the classical welfare models or they regroup themselves into new circumstances’ adapted models. Even though the classical welfare models are generally still checked up with the …
Contribution to a Taxonomic Revision of the Sicilian Helichrysum Taxa by PCA Analysis of Their Essential-Oil Compositions
2016
The chemical profile of the essential oils in ten populations of the genus Helichrysum Mill. (Asteraceae), collected in the loci classici of the nomenclatural types of the taxa endemic to Sicily, were analyzed. Our results confirm that the analysis of secondary metabolites can be used to fingerprint wild populations of Helichrysum, the chemical profiles being coherent with the systematic arrangement of the investigated populations in three main clusters, referring to the aggregates of H. stoechas, H. rupestre, and H. italicum, all belonging to the section Stoechadina. The correct nomenclatural designation of the investigated populations is discussed and the following two new combinations ar…
Detection of batch effects in liquid chromatography-mass spectrometry metabolomic data using guided principal component analysis.
2014
Metabolomics based on liquid chromatography-mass spectrometry (LC-MS) is a powerful tool for studying dynamic responses of biological systems to different physiological or pathological conditions. Differences in the instrumental response within and between batches introduce unwanted and uncontrolled data variation that should be removed to extract useful information. This work exploits a recently developed method for the identification of batch effects in high throughput genomic data based on the calculation of a delta statistic through principal component analysis (PCA) and guided PCA. Its applicability to LC-MS metabolomic data was tested on two real examples. The first example involved t…
Increased serum miR-193a-5p during non-alcoholic fatty liver disease progression: Diagnostic and mechanistic relevance
2022
Background & Aims Serum microRNA (miRNA) levels are known to change in non-alcoholic fatty liver disease (NAFLD) and may serve as useful biomarkers. This study aimed to profile miRNAs comprehensively at all NAFLD stages. Methods We profiled 2,083 serum miRNAs in a discovery cohort (183 cases with NAFLD representing the complete NAFLD spectrum and 10 population controls). miRNA libraries generated by HTG EdgeSeq were sequenced by Illumina NextSeq. Selected serum miRNAs were profiled in 372 additional cases with NAFLD and 15 population controls by quantitative reverse transcriptase PCR. Results Levels of 275 miRNAs differed between cases and population controls. Fewer differences were seen wi…
A new methodology for Functional Principal Component Analysis from scarce data. Application to stroke rehabilitation.
2015
Functional Principal Component Analysis (FPCA) is an increasingly used methodology for analysis of biomedical data. This methodology aims to obtain Functional Principal Components (FPCs) from Functional Data (time dependent functions). However, in biomedical data, the most common scenario of this analysis is from discrete time values. Standard procedures for FPCA require obtaining the functional data from these discrete values before extracting the FPCs. The problem appears when there are missing values in a non-negligible sample of subjects, especially at the beginning or the end of the study, because this approach can compromise the analysis due to the need to extrapolate or dismiss subje…
Survival, morphological variability, and performance of Opuntia ficus-indica in a semi-arid region of India
2022
Cactus pear (Opuntia ficus-indica (L.) Mill.) can survive extreme environmental condition and is known for its fodder potential in many parts of the world. The morphological diversity of 15 introduced accessions was evaluated at Jhansi, Uttar Pradesh, India. The plants were established in 2013. Survival and nutrient status were evaluated after two years. Above-ground plant height, biomass, primary and secondary cladode numbers, primary and secondary cladode lengths and below-ground root length, weight, and surface area measurements were done six years after cladode planting. Yellow San Cono, White Roccapalumba, and Seedless Roccapalumba survived 100%. The discriminant traits according to pr…
Weighted samples, kernel density estimators and convergence
2003
This note extends the standard kernel density estimator to the case of weighted samples in several ways. In the first place I consider the obvious extension by substituting the simple sum in the definition of the estimator by a weighted sum, but I also consider other alternatives of introducing weights, based on adaptive kernel density estimators, and consider the weights as indicators of the informational content of the observations and in this sense as signals of the local density of the data. All these ideas are shown using the Penn World Table in the context of the macroeconomic convergence issue.