Search results for "Component analysis"
showing 10 items of 562 documents
Semi-blind Independent Component Analysis of functional MRI elicited by continuous listening to music
2013
This study presents a method to analyze blood-oxygen-level-dependent (BOLD) functional magnetic resonance imaging (tMRI) signals associated with listening to continuous music. Semi-blind independent component analysis (ICA) was applied to decompose the tMRI data to source level activation maps and their respective temporal courses. The unmixing matrix in the source separation process of ICA was constrained by a variety of acoustic features derived from the piece of music used as the stimulus in the experiment. This allowed more stable estimation and extraction of more activation maps of interest compared to conventional ICA methods.
Snowball ICA: A Model Order Free Independent Component Analysis Strategy for Functional Magnetic Resonance Imaging Data
2020
In independent component analysis (ICA), the selection of model order (i.e., number of components to be extracted) has crucial effects on functional magnetic resonance imaging (fMRI) brain network analysis. Model order selection (MOS) algorithms have been used to determine the number of estimated components. However, simulations show that even when the model order equals the number of simulated signal sources, traditional ICA algorithms may misestimate the spatial maps of the signal sources. In principle, increasing model order will consider more potential information in the estimation, and should therefore produce more accurate results. However, this strategy may not work for fMRI because …
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…
Using SOM and PCA for analysing and interpreting data from a P-removal SBR
2008
This paper focuses on the application of Kohonen self-organizing maps (SOM) and principal component analysis (PCA) to thoroughly analyse and interpret multidimensional data from a biological process. The process is aimed at enhanced biological phosphorus removal (EBPR) from wastewater. In this work, SOM and PCA are firstly applied to the data set in order to identify and analyse the relationships among the variables in the process. Afterwards, K-means algorithm is used to find out how the observations can be grouped, on the basis of their similarity, in different classes. Finally, the information obtained using these intelligent tools is used for process interpretation and diagnosis. In the…
A voltammetric e-tongue tool for the emulation of the sensorial analysis and the discrimination of vegetal milks
2018
[EN] The relevance of plant-based food alternatives to dairy products, such as vegetable milks, has been growing in recent decades, and the development of systems capable of classifying and predicting the sensorial profile of such products is interesting. In this context, a methodology to perform the sensorial analysis of vegetable milks (oat, soya, rice, almond and tiger nut), based on 12 parameters, was validated. An electronic tongue based on the combination of eight metals with pulse voltammetry was also tested. The current intensity profiles are characteristic for each non-dairy milk type. Data were processed with qualitative (PCA, dendrogram) and quantitative (PLS) tools. The PCA stat…
Extraction of ERP from EEG data
2007
In this article, a simple but novel technique for extracting a linear subspace related to event related potentials (ERPs) from ElectroEncephaloGraphy (EEG) data is introduced. The technique consists of a sequence of basic linear operations applied to multidimensional EEG data in a problem-specific manner. The derivation of the proposed technique is given and results with real data are described together with overall conclusions.
Spectral properties of correlation matrices for some hierarchically nested factor models
2007
We show that spectral methods, such as Principal Component Analysis and Random Matrix Theory, are unable to reveal the hierarchical (or nested) structure of a set of mutivariate data. We consider the method introduced in M. Tumminello et al., EPL 78, 30006 (2007) to associate a hierarchical factor model with a set of data by making use of clustering algorithms. This is done by proving the existence of a bijective correspondence between a hierarchical tree and a factor model.
<strong>New tool useful for drug discovery validated through benchmark datasets</strong>
2018
Atomic Weighted Vectors (AWVs) are vectors that contain the codified information of molecular structures, which can apply to a set of Aggregation Operators (AOs) to calculate total and local molecular descriptors (MDs). This article presents an exploratory study of a new tool useful for drug discovery using different datasets, such as DRAGON and Sutherland’s datasets, as well as their comparison with other well-known approaches. In order to evaluate the performance of the tool, several statistics and QSAR/QSPR experiments were performed. Variability analyses are used to quantify the information content of the AWVs obtained from the tool, by the way of an information theory-based algorithm. …
A Comparative Study and an Evaluation Framework of Multi/Hyperspectral Image Compression
2009
In this paper, we investigate different approaches for multi/hyperspectral image compression. In particular, we compare the classic multi-2D compression approach and two different implementations of 3D approach (full 3D and hybrid) with regards to variations in spatial and spectral dimensions. All approaches are combined with a weighted Principal Component Analysis (PCA) decorrelation stage to optimize performance. For consistent evaluation, we propose a larger comparison framework than the conventionally used PSNR, including eight metrics divided into three families. The results show the weaknesses and strengths of each approach.
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…