In many multivariate statistical techniques, a set of linear functions of the original p variables is produced. One of the more difŽ cult aspects of these techniques is the interpretation of the linear functions, as these functions usually have nonzero coefŽ cients on all p variables. A common approach is to effectively ignore (treat as zero) any coefŽ cients less than some threshold value, so ...

Contrary to many other deformable models Active Shape Models (ASM) represents a general way of performing non-rigid object segmentation. Shape variation is extracted from a training set by applying principal component analysis to point distribution models, rather than hand crafting a priori knowledge into the model. In this paper we investigate di erent properties of ASM. Topics treated are the...

We propose a new technique for developing ad-hoc numerical abstract domains by means of statistical analysis. We apply Principal Component Analysis to partial execution traces of programs, to find out a “best basis” in the vector space of program variables. This basis may be used to specialize numerical abstract domains, in order to enhance the precision of the analysis. As an example, we apply...

As a rational approach to the many lung function tests available, we have subjected the results of a battery of six lung function measurements made in 458 coalminers to the statistical technique of principal components analysis. By this means the six test results were reduced to three principal components without important loss of information. The first component appeared to represent lung size...

In recent years, many algorithms based on kernel principal component analysis (KPCA) have been proposed including kernel principal component regression (KPCR). KPCR can be viewed as a non-linearization of principal component regression (PCR) which uses the ordinary least squares (OLS) for estimating its regression coefficients. We use PCR to dispose the negative effects of multicollinearity in ...

‎The most challenging task in dealing with Bayesian networks is learning their structure‎. ‎Two classical approaches are often used for learning Bayesian network structure;‎ ‎Constraint-Based method and Score-and-Search-Based one‎. ‎But neither the first nor the second one are completely satisfactory‎. ‎Therefore the heuristic search such as Genetic Alg...

abstract many researchers have recently considered the use of data obtained from remote sensing (r.s) to reveal changes. in the present study, we have attempted to reveal the morphodynamic changes in taleghan river basin through image satellite and by analyzing the principal component analysis (pca) and fuzzy logic. the final map indicates the intensity of changes through, 15-years interval obs...

background: cutaneous leishmaniasis is one of the most important parasitic diseases in humans. in this disease, one of the responsible organisms is leishmania major, which is transmitted by sandfly vector. there are specific differences in biochemical profiles and metabolite pathways in logarithmic and stationary phases of leishmania parasites. in the present study, 1h nmr spectroscopy was used...