The aim of this study is to propose a new robust face recognition algorithm by combining principal component analysis (PCA), Triplet Similarity Embedding based technique and Projection as similarity metric at the different stages processes. main idea use PCA for feature extraction dimensionality reduction, then train triplet embedding accommodate changes in facial poses, finally orthogonal proj...

Neuropsychological assessment aids in the diagnosis of Alzheimer's disease (AD) by objectively establishing cognitive impairment from standardized tests. We present new criteria for diagnosis that use weighted combined scores from multiple tests. Our method employs two multivariate analyses: principal components analysis (PCA) and discriminant analysis. PCA (N = 216 participants) created more i...

PCA has find out its most important application in the field of linear algebra. PCA is a method of extracting information from confusing data sets so used in various fields like neuroscience, computer graphics, etc [19]. PCA is a vector space transform often used to reduce multidimensional data sets to lower dimensions for analysis. Depending on the field of application, it is also named the di...

In this paper, we present a novel approach for unsupervised change detection on multi-spectral satellite images. The advantage of unsupervised approach over the supervised one is that the generation of an appropriated ground truth is not required. Especially, when the ground truth is not available, the unsupervised approach is the fundamental one. The unsupervised change detection method used i...

Principal component analysis (PCA) has been widely used in analyzing high-dimensional data. It converts a set of observed data points possibly correlated variables into linearly uncorrelated via an orthogonal transformation. To handle streaming and reduce the complexities PCA, (subspace) online PCA iterations were proposed to iteratively update transformation by taking one point at time. Existi...

morphophysiological variations of field bindweed populations in tehran province was studied during 2006 and 2007 growing seasons using multivariate analysis methods. to determine the variations, 43 morphological and physiological characters were considered biometrically.  the main characters at principal component analysis (pca) consisted of leaf dry weight, shoot dry weight, and leaf area to i...

A new method for speech signal reconstruction is proposed by performing a nonlinear Kernel Principal Component Analysis (KPCA). By the use of kernel functions, one can efficiently compute principal components in high-dimensional feature spaces, and reconstruct vectors mapping from input space by those dominant principal components. As the reconstructed vectors is expressed in high dimensional f...

Principal coordinate analysis (PCO), as a duality of principal component analysis (PCA), is also a classical method for exploratory data analysis. In this paper we propose a probabilistic PCO by using a normal latent variable model in which maximum likelihood estimation and an expectation-maximization algorithm are respectively devised to calculate the configurations of objects in a lowdimensio...

Identi cation of independent physical/dynamical modes and corresponding principal component time series is an important aspect of climate studies for they serve as a tool for detecting and predicting climate changes. While there are a number of di erent eigen techniques their performance for identifying independent modes varies. Considered here are comparison tests of eight eigen techniques in ...