functional magnetic resonance imaging (fmri) is a safe and non-invasive way to assess brain functions by using signal changes associated with brain activity. the technique has become a ubiquitous tool in basic, clinical and cognitive neuroscience. this method can measure little metabolism changes that occur in active part of the brain. we process the fmri data to be able to find the parts of br...

Principal component analysis (PCA) is an important tool in exploring data. The conventional approach to PCA leads to a solution which favours the structures with large variances. This is sensitive to outliers and could obfuscate interesting underlying structures. One of the equivalent definitions of PCA is that it seeks the subspaces that maximize the sum of squared pairwise distances between d...

Principal component analysis (PCA) is a well-known classical data analysis technique. There are a number of algorithms for solving the problem, some scaling better than others to problems with high dimensionality. They also differ in their ability to handle missing values in the data. We study a case where the data are high-dimensional and a majority of the values are missing. In case of very s...

We modify the recently proposed forecasting model of high-dimensional covariance matrices (HDCM) asset returns using principal component analysis (PCA). It is well-known that when sample size smaller than dimension, eigenvalues estimated by classical PCA have a bias. In particular, very small number are extremely large and they called spiked eigenvalues. High-dimensional gives which correct bia...

Principal Component Analysis (PCA) is a fundamental data preprocessing tool in the world of machine learning. While PCA often thought as dimensionality reduction method, purpose actually two-fold: dimension and uncorrelated feature Furthermore, enormity dimensions sample size modern day datasets have rendered centralized solutions unusable. In that vein, this paper reconsiders problem when samp...

In this paper we propose to analyze the variability of brain structures using principal component analysis (PCA). We rely on a data base of registered and segmented 3D MRI images of normal subjects. We propose to use as input of PCA sampled points on the surface of the considered objects, selected using uniformity criteria or based on mean and Gaussian curvatures. Results are shown on the later...

Abstract Hedonic models in environmental valuation studies have grown in terms of number of transactions and number of explanatory variables.We focus on the practical challenge of model reduction, when aiming for reliable parsimonious models, sensitive to omitted variable bias and multicollinearity. We evaluate two common model reduction approaches in an empirical case. The first relies on a pr...

Principal component analysis (PCA) is widely used in data processing and dimensionality reduction. However, PCA suffers from the fact that each principal component is a linear combination of all the original variables, thus it is often difficult to interpret the results. We introduce a new method called sparse principal component analysis (SPCA) using the lasso (elastic net) to produce modified...

Meat, as an important source of protein, is one of the main parts of many people’s diet. Due toeconomic interests and thereupon adulteration, there are special concerns on its accurate labeling.In this study Fourier transform infrared (ATR-FTIR) spectroscopy combined with chemometrictechniques (principal component analysis (PCA), artificial neural networks (ANNs), and partial<...

Principal Components Analysis (PCA), which is more recently referred to as Proper Orthogonal Decomposition (POD) in the literature, is a popular technique in many fields of engineering, science, and mathematics for analysis of time series data. The benefit of PCA for dynamical systems comes from its ability to detect and rank the dominant coherent spatial structures of dynamic response, such as...