Principal Components Analysis (PCA), which is more recently referred to as Proper Orthogonal Decomposition (POD) in the literature, is a popular technique in many fields of engineering, science, and mathematics for analysis of time series data. The benefit of PCA for dynamical systems comes from its ability to detect and rank the dominant coherent spatial structures of dynamic response, such as...

The goal of speech enhancement varies according to specific applications, such as to reduce listener fatigue, to boost the overall speech quality, to increase intelligibility, and to improve the performance of the voice communication device. This paper presents Multiscale principal component analysis (MSPCA) for denoising of single channel speech signal. Principle Component Analysis (PCA) is a ...

A best evidence topic in thoracic surgery was written according to a structured protocol. The question addressed was 'is the addition of ketamine to morphine patient-controlled analgesia (PCA) following thoracic surgery superior to morphine alone'. Altogether 201 papers were found using the reported search, of which nine represented the best evidence to answer the clinical question. The authors...

Prostate cancer (PCa) is a common illness for aging males. Lycopene has been identified as an antioxidant agent with potential anticancer properties. Studies investigating the relation between lycopene and PCa risk have produced inconsistent results. This study aims to determine dietary lycopene consumption/circulating concentration and any potential dose–response associations with the risk of ...

Usually, principal components analysis is carried out by calculating the eigenvalues and eigenvectors of the correlation matrix. With N cases and P variables, if we write X for the N × P matrix which has been standardised so that columns have zero mean and unit standard deviation, we find the eigenvalues and eigenvectors of the P × P matrix X.X (which is N or (N − 1) times the correlation matri...

Mercer kernels are used for a wide range of image and signal processing tasks like de-noising, clustering, discriminant analysis etc. These algorithms construct their solutions in terms of the expansions in a high-dimensional feature space F. However, many applications like kernel PCA (principal component analysis) can be used more effectively if a pre-image of the projection in the feature spa...

Methods for analysis of principal components in discrete data have existed for some time under various names such as grade of membership modelling, probabilistic latent semantic analysis, and genotype inference with admixture. In this paper we explore a number of extensions to the common theory, and present some application of these methods to some common statistical tasks. We show that these m...

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...

PURPOSE Opioid-based intravenous patient-controlled analgesia (IV PCA) is popular method of postoperative pain control, but many patients suffer from IV PCA-related postoperative nausea and vomiting (PONV). In this retrospective observational study, we have determined independent predictors of IV PCA-related PONV and predictive values of the Apfel's simplified risk score in pursuance of identif...

This paper introduces a parametric space to describe the shape of human breasts. The parameter space has been obtained from a sample of about 40 patient’s MRI taken in prone position. The data have been cleaned from noise and disturbances and has been dimensionally reduced using Principal Component Analysis. If two references relative to extremal shapes (one of a reconstructed breast and one of...