Principal component analysis denotes a popular algorithmic technique to dimension reduction and factor extraction. Spatial variants have been proposed to account for the particularities of spatial data, namely spatial heterogeneity and spatial autocorrelation, and we present a novel approach which transfers principal component analysis into the spatio-temporal realm. Our approach, named stPCA, ...

this paper presents a combination of data envelopment analysis (dea) and principal component analysis (pca) to reduce the dimensionality of data set. dea is known as effective tool for assessment and benchmarking. the weak point of dea, it is that the number of efficient dmus relies on the number of variables (inputs and outputs). for solving this, first, we do principal component analysis (pca...

Multivariate statistical techniques, such as cluster analysis and principal component analysis (PCA), were applied for evaluation of spatial variations and interpretation of large complex water quality data set of the Ganga river basin, generated during one year (2013-2014) monitoring of eight water parameters at seven different sites. Hierarchical cluster analysis grouped seven sampling sites ...

This paper presents an introduction to independent component analysis (ICA). Unlike principal component analysis, which is based on the assumptions of uncorrelatedness and normality, ICA is rooted in the assumption of statistical independence. Foundations and basic knowledge necessary to understand the technique are provided hereafter. Also included is a short tutorial illustrating the implemen...

inter- and intra-population variations of a recently described endemic cyprinid fish alburnoides qanati coad and bogutskaya, 2009 collected from three localities in the kor river basin, iran, were studied by applying morphometric and meristic characters using canonical discriminate function analysis (dfa), principal component analysis (pca) and cluster analysis (ca). in dfa, the overall random ...

Data mining is a collection of analytical techniques to uncover new trends and patterns in massive databases. These data mining techniques stress visualization to thoroughly study the structure of data and to check the validity of the statistical model fit which leads to proactive decision making. Principal component analysis (PCA) is one of the unsupervised data mining tools used to reduce dim...

In this paper, we shall propose a new method for the copyright protection of digital images. To embed the watermark, our new method partitions the original image into blocks and uses the PCA function to project these blocks onto a linear subspace. There is a watermark table, which is computed from projection points, kept in our new method. When extracting a watermark, our method projects the bl...