The method of Principal Components Analysis (PCA) is widely used in statistical data analysis for engineering and the sciences. It is an effective tool for reducing the dimensionality of datasets while retaining majority of the data information. This paper explores the method of using PCA for spacecraft pose estimation for the purpose of proximity operations, and adapts a novel kernel based PCA...

Two-dimensional principal components analysis (2DPCA) needs more coefficients than principal components analysis (PCA) for image representation and hence needs more time for classification. The bidirectional PCA (BDPCA) is proposed to overcome these drawbacks of 2DPCA. Both 2DPCA and BDPCA, however, can work only in Euclidean space. In this paper, we propose Laplacian BDPCA (LBDPCA) representat...

Kernel Principal Component Analysis (KPCA) is a dimension reduction method that is closely related to Principal Component Analysis (PCA). This report gives an overview of kernel PCA and presents an implementation of the method in MATLAB. The implemented method is tested in a transductive setting on two data bases: Iris data and sugar data. Two methods for labeling data points are considered, th...

Principal component analysis (PCA) can be seen as a singular value decomposition (SVD) of a column-centred data matrix. In a number of applications, no pre-processing of the data is carried out, and it is the uncentred data matrix that is subjected to an SVD, in what is often called an uncentred PCA. This paper explores the relationships between the results from both the standard, column-centre...

A complete Bayesian framework for Principal Component Analysis (PCA) is proposed in this paper. Previous model-based approaches to PCA were usually based on a factor analysis model with isotropic Gaussian noise. This model does not impose orthogonality constraints, contrary to PCA. In this paper, we propose a new model with orthogonality restrictions, and develop its approximate Bayesian soluti...

Principal Component Analysis (PCA) is often used for reducing the dimensionality of input feature space. However, the eigenspace based on PCA is not always the best feature space for pattern recognition. In this paper, we use the feature space based on Independent Component Analysis (ICA) and show that the ICA representation is more effective than the PCA representation for human action recogni...

Principal Component Analysis (PCA) has been widely used for efficient representation of face images data in a low dimensional subspace. In this study, we use PCA to analyse different properties of faces, such as gender, ethnicity, age and identity. Using Linear Discriminant Analysis (LDA), we show that PCA efficiently encodes information related to different properties, different components of ...

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...

In this paper we describe a face recognition method based on PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis). The method consists of two steps: rst we project the face image from the original vector space to a face subspace via PCA, second we use LDA to obtain a best linear clas-siier. The basic idea of combining PCA and LDA is to improve the generalization capability ...