Principal component analysis (PCA) is a key statistical technique for multivariate data analysis. For large data sets, the common approach to PCA computation is based on the standard NIPALS-PCA algorithm, which unfortunately suffers from loss of orthogonality, and therefore its applicability is usually limited to the estimation of the first few components. Here we present an algorithm based on ...

This paper discusses principal component analysis (PCA) of integral transforms (spectra and autocovariance functions) of time-domain signals. It is illustrated using acoustic emissions from mechanical equipment. It was found that acoustic signals from different stages of operation appeared as distinct clusters in the PCA analysis. The clusters moved when machinery faults were present and the mo...

Analysing the face recognition rate of various current face recognition algorithms is absolutely critical in developing new robust algorithms. In his paper we report performance analysis of Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) for face recognition. This analysis was carried out on various current PCA and LDA based face recognition algorithms using standard p...

In this paper, the performances of appearance-based statistical methods such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Independent Component Analysis (ICA) are tested and compared for the recognition of colored face images. Three sets of experiments are conducted for relative performance evaluations. In the first set of experiments, the recognition performanc...

functional magnetic resonance imaging (fmri) is a safe and non-invasive way to assess brain functions by using signal changes associated with brain activity. the technique has become a ubiquitous tool in basic, clinical and cognitive neuroscience. this method can measure little metabolism changes that occur in active part of the brain. we process the fmri data to be able to find the parts of br...

PURPOSE The purpose of this paper is to examine differences between two factor analytical methods and their relevance for symptom cluster research: common factor analysis (CFA) versus principal component analysis (PCA). METHODS Literature was critically reviewed to elucidate the differences between CFA and PCA. A secondary analysis (N = 84) was utilized to show the actual result differences f...

BACKGROUND Despite evidence suggesting roles for caspase-8 (CASP8) -652 6N del and D302H polymorphisms in prostate cancer (PCa), the association of these polymorphisms with PCa risk remains inconclusive. Therefore, a meta-analysis was performed to more precisely estimate the association of CASP8 -652 6N del and D302H polymorphisms with PCa susceptibility. MATERIALS AND METHODS A comprehensive...

This paper focuses on the application and comparison of Principal Component Analysis (PCA) and Independent Component Analysis (ICA) using two generic artificially created datasets. PCA and ICA are assessed in terms of their abilities to infer reference spectra and to estimate relative concentrations of the constituent compounds present in the analysed samples. The results show that ICA outperfo...

Face recognition is one of the Biometric characteristics for person identification. In this paper, Face recognition is done using two feature extraction techniques PCA (Principal Component Analysis) and MPCA (Modular Principal Component Analysis). PCA is a linear projection method in which dimensionality reduction is applied to the original image space. MPCA is an improved version of PCA in whi...

The selection of appropriate wavelets is an important target for any application. In this paper Face recognition has been performed using Principal component analysis (PCA), Gaussian based PCA and Gabor based PCA. PCA extracts the relevant information from complex data sets and provides a solution to reduce dimensionality. PCA is based on Euclidean distance calculation which is minimized by app...