The method of sparse principal component analysis (S-PCA) proposed by Zou, Hastie, and Tibshirani (2006) is an attractive approach to obtain sparse loadings in principal component analysis (PCA). S-PCA was motivated by reformulating PCA as a least-squares problem so that a lasso penalty on the loading coefficients can be applied. In this article, we propose new estimates to improve S-PCA in the...

this paper is based on a combination of the principal component analysis (pca), eigenface and support vector machines. using n-fold method and with respect to the value of n, any person’s face images are divided into two sections. as a result, vectors of training features and test features are obtain ed. classification precision and accuracy was examined with three different types of kernel and...

This article analyses processes of change undergone by Spanish medium-sized cities during 1981–2011 on the one hand, and 2000–2018 other, as they are different sources. We established a classification to show importance this type city starting from hypothesis that process is generalised in which behave according their position territory. The dynamics predominantly associated with contexts econo...

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...

Seasonal variation in water quality of Anchar Lake was evaluated using multivariate statistical techniques- principal component analysis (PCA) and cluster analysis (CA). Water quality data collected during 4 seasons was analyzed for 13 parameters. ANOVA showed significant variation in pH (F3 = 10.86, P < 0.05), temperature (F3 = 65, P

Seasonal variation in water quality of Anchar Lake was evaluated using multivariate statistical techniques- principal component analysis (PCA) and cluster analysis (CA). Water quality data collected during 4 seasons was analyzed for 13 parameters. ANOVA showed significant variation in pH (F3 = 10.86, P < 0.05), temperature (F3 = 65, P