Principal component analysis (PCA) is a ubiquitous statistical technique for data analysis. PCA is however limited by its linearity and may sometimes be too simple for dealing with real-world data especially when the relations among variables are nonlinear. Recent years have witnessed the emergence of nonlinear generalizations of PCA, as for instance nonlinear principal component analysis (NLPC...

Around the world, peasant populations continue their traditional livelihoods in the face of increasingly global economic transformation. In Iran, as elsewhere, agriculture is one of the most important economic sectors and majority of farmers are peasants who still farm small plots of land, usually in marginal environments utilizing traditional and subsistence methods, while their contribution t...

Principal component analysis (PCA) is an orthogonal transformation that seeks the directions of maximum variance in the data and is commonly used to reduce the dimensionality of the data. In image denoising, a compromise has to be found between noise reduction and preserving significant image details. PCA is a statistical technique for simplifying a dataset by reducing datasets to lower dimensi...

Principal component analysis (PCA) is a well-known statistical processing technique that allows to study the correlations among the components of multivariate data and to reduce redundancy by projecting the data over a proper basis. The PCA may be performed both in a batch method and in a recursive fashion; the latter method has been proven to be very effective in presence of high dimension dat...

This paper presents a new nonlinear multivariate statistical process control technique for identifying and isolating the root cause of abnormal process behavior. The new technique is a nonlinear extension to the variables reconstruction technique by (Dunia et al., 1996), based on nonlinear principal component analysis (NLPCA). This work demonstrates that the variable reconstruction (i) affects ...

Modern brain imaging techniques like voltage sensitive dye recording get highly resolved complex spatio-temporal datasets. For temporal classification of these complex 2D + t datasets we applied several statistical analysis approaches like principal component analysis and cluster analysis. These methods proved to be very helpful to characterise the cortical response patterns into known and unkn...