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PubMed1997

Using principal component analysis to describe wound status.

Principal component (PC) analysis was used to assess the status of pressure ulcers over time in 37 subjects. Content validity was established by literature review and expert opinion. The primary variables in the wound healing model are ulcer surface area, exudate amount, and surface appearance. A linear function based on PC analysis of these values was established at each time point with an appropriate weighting of 2:3:3. This function explained 55% to 65% of the variation in the data. Other variables were considered in the model as well. However, because of the correlation of these variables with the original three, the modeling was not improved. A decreasing function from week 0 to week 8 established a good linear fit to the data and reasonable discrimination between time points, given the limitations of the sample size. There is good discrimination among the time points, at least for the earlier times compared with the later times. The procedure, although promising, requires validation with a larger data set.

Discriminant Analysis↗
PubMed2003

A constrained EM algorithm for principal component analysis.

We propose a constrained EM algorithm for principal component analysis (PCA) using a coupled probability model derived from single-standard factor analysis models with isotropic noise structure. The single probabilistic PCA, especially for the case where there is no noise, can find only a vector set that is a linear superposition of principal components and requires postprocessing, such as diagonalization of symmetric matrices. By contrast, the proposed algorithm finds the actual principal components, which are sorted in descending order of eigenvalue size and require no additional calculation or postprocessing. The method is easily applied to kernel PCA. It is also shown that the new EM algorithm is derived from a generalized least-squares formulation.

Algorithms↗
PubMed1999

Psychopathology in the Dogon Plateau: an assessment using the QDSM and principal components analysis.

BACKGROUND: The present paper reports the findings of principal components analysis performed on the basis of answers to the Questionnaire pour le Depistage en Santé Mentale (QDSM) administered to subjects from the Bandiagara plateau (Mali), who had been evaluated in a previously published report. METHODS: The study sample was made up of 466 subjects (253 males, 213 females), 273 of whom belonged to the Dogon ethnic group, 163 were Peul and the remaining 30 belonged to other groups (Sonrai, Bozo, Tuareg, Bambara). All subjects were submitted to QDSM, a structured interview derived from the Self Reporting Questionnaire. Data obtained were processed by means of principal components analysis, in order to obtain syndromic aggregations. RESULTS: Eight factors with an Eigen value greater than 1 were extracted, which provided sufficient explanation for the overall variance observed among the 23 items. These factors may be termed as follows: Sadness (factor 1); Dysphoria (factor 2); Nightmares (factor 3); Persecution (factor 4); Somatic symptoms (factor 5); Special powers (factor 6); Hopelessness (factor 7); Loss of Interest (factor 8). CONCLUSIONS: The findings from this study support the hypothesis of an independence of "psychosomatic" from depressive symptoms. In particular, contrary to some evidence derived from other African studies, the present research appears to suggest a possible counterposition of these two ways of expressing depression, commonly considered as autonomous.

Adolescent↗
PubMed2002

Main functional roles of knee flexors/extensors in able-bodied gait using principal component analysis (I).

This study was undertaken to demonstrate how principal component analysis (PCA) can be used to detect the main functional structure of actions taken by knee flexors/extensors during able-bodied gait. PCA was applied as a classification and curve structure detection method for knee sagittal muscle moment developed during walking of 20 young, healthy male subjects. Over 90% of the information provided by the first three principal components (PCs) was chosen for further biomechanical interpretation. PCA was able to identify the three main functional contributions of knee sagittal muscle moment during able-bodied gait, namely control balance, foot clearance/limb preparation and shock absorption.

Adolescent↗
PubMed2005

Iterative kernel principal component analysis for image modeling.

In recent years, Kernel Principal Component Analysis (KPCA) has been suggested for various image processing tasks requiring an image model such as, e.g., denoising or compression. The original form of KPCA, however, can be only applied to strongly restricted image classes due to the limited number of training examples that can be processed. We therefore propose a new iterative method for performing KPCA, the Kernel Hebbian Algorithm which iteratively estimates the Kernel Principal Components with only linear order memory complexity. In our experiments, we compute models for complex image classes such as faces and natural images which require a large number of training examples. The resulting image models are tested in single-frame super-resolution and denoising applications. The KPCA model is not specifically tailored to these tasks; in fact, the same model can be used in super-resolution with variable input resolution, or denoising with unknown noise characteristics. In spite of this, both super-resolution and denoising performance are comparable to existing methods.

Algorithms↗
PubMed2004

Coupled principal component analysis.

A framework for a class of coupled principal component learning rules is presented. In coupled rules, eigenvectors and eigenvalues of a covariance matrix are simultaneously estimated in coupled equations. Coupled rules can mitigate the stability-speed problem affecting noncoupled learning rules, since the convergence speed in all eigendirections of the Jacobian becomes widely independent of the eigenvalues of the covariance matrix. A number of coupled learning rule systems for principal component analysis, two of them new, is derived by applying Newton's method to an information criterion. The relations to other systems of this class, the adaptive learning algorithm (ALA), the robust recursive least squares algorithm (RRLSA), and a rule with explicit renormalization of the weight vector length, are established.

Principal Component Analysis↗
PubMed2006

Megavariate analysis of environmental QSAR data. Part I--a basic framework founded on principal component analysis (PCA), partial least squares (PLS), and statistical molecular design (SMD).

This paper introduces principal component analysis (PCA), partial least squares projections to latent structures (PLS), and statistical molecular design (SMD) as useful tools in deriving multi- and megavariate quantitative structure-activity relationship (QSAR) models. Two QSAR data sets from the fields of environmental toxicology and environmental chemistry are worked out in detail, showing the benefits of PCA, PLS and SMD. PCA is useful when overviewing a data set and exploring relationships among compounds and relationships among variables. PLS is the regression extension of PCA and is used for establishing QSARs. SMD is essential for selecting informative training and test sets of compounds for QSAR calibration and validation.

Data Interpretation, Statistical↗
PubMed2003

Architectural measures of the cancellous bone of the mandibular condyle identified by principal components analysis.

As several morphological parameters of cancellous bone express more or less the same architectural measure, we applied principal components analysis to group these measures and correlated these to the mechanical properties. Cylindrical specimens (n = 24) were obtained in different orientations from embalmed mandibular condyles; the angle of the first principal direction and the axis of the specimen, expressing the orientation of the trabeculae, ranged from 10 degrees to 87 degrees. Morphological parameters were determined by a method based on Archimedes' principle and by micro-CT scanning, and the mechanical properties were obtained by mechanical testing. The principal components analysis was used to obtain a set of independent components to describe the morphology. This set was entered into linear regression analyses for explaining the variance in mechanical properties. The principal components analysis revealed four components: amount of bone, number of trabeculae, trabecular orientation, and miscellaneous. They accounted for about 90% of the variance in the morphological variables. The component loadings indicated that a higher amount of bone was primarily associated with more plate-like trabeculae, and not with more or thicker trabeculae. The trabecular orientation was most determinative (about 50%) in explaining stiffness, strength, and failure energy. The amount of bone was second most determinative and increased the explained variance to about 72%. These results suggest that trabecular orientation and amount of bone are important in explaining the anisotropic mechanical properties of the cancellous bone of the mandibular condyle.

Biomechanical Phenomena↗
PubMed1986

Spectral methods for principal components analysis of event-related brain potentials.

Principal components analysis has been a widely used method for the analysis of event-related, electrical brain potentials (ERPs). Recent emphasis has been placed on measuring the topography of ERPs, as derived from the instantaneous measurements from multiple locations, and on defining diagnostic differences in ERPs among various clinical populations. One goal of the present paper is to discuss inherent difficulties in utilizing PCA as an analytical technique in multiple location and multiple group studies. Another goal is to demonstrate the utility of spectral analysis and its equivalency to PCA when the signal imbedded in stationary noise model is used. Spectral analysis readily permits analysis of multiple lead multiple group studies.

Brain↗
PubMed1999

Head and neck cancer: detection of recurrence with three-dimensional principal components analysis at dynamic FDG PET.

Fully automated principal components analysis (PCA) was applied to dynamic 2-[fluorine-18]fluoro-2-deoxy-D-glucose (FDG) positron emission tomographic (PET) images obtained in 15 patients with previously treated head and neck cancer. PCA with time-activity curves incorporated kinetic information about FDG uptake, which improved tissue characterization on FDG PET images. The combination of standardized uptake value and PCA image sets likely will improve the reliability of tumor detection in head and neck cancers.

Adult↗
PubMed2004

Analysis of petal shape variation of Primula sieboldii by elliptic fourier descriptors and principal component analysis.

BACKGROUND AND AIMS: Petals are important for Primula sieboldii because of the commercial value of its flowers, and their form is a target characteristic for breeding. An appropriate understanding of petal form in terms of genetic mechanisms and environmental effects is necessary for improvement of this species. The aim of this study was to establish a quantitative evaluation method of petal shape by elliptic Fourier descriptors and principal component analysis (EF-PCA), and thus to investigate genotypic and environmental effects on petal morphology. METHODS: EF-PCA describes an overall shape mathematically by transforming coordinate information concerning its contours into elliptic Fourier descriptors (EFDs) and summarizing the EFDs by principal component analysis. To examine varietal effects on principal component (PC) scores and petal area among commercial varieties, nested ANOVAs were performed (since the samples had a hierarchical structure with four sources, i.e. variety, plant, flower and petal). KEY RESULTS: Petal shape variation could be evaluated successfully and the symmetrical and asymmetrical elements of the overall shape variation could be detected. The proportions of the variance component due to varietal differences were more than 70 % in the first five PCs of the symmetrical elements and petal area. By contrast, the proportions due to varietal effects of all PCs of the asymmetrical elements were less than 20 %, and the proportions of the variation within a flower were more than 75 %. It was also demonstrated that the yearly variance of petal shape was small, and that of petal area was large. CONCLUSIONS: Within a flower the major source of the symmetrical elements is genotypic and the asymmetrical elements are strongly affected by the environment. With respect to petal area, the contribution of genotypes is also large; it is, however, affected by the macro-environment more notably than is petal shape.

Analysis of Variance↗
PubMed2004

Aggregate eco-efficiency indices for New Zealand--a principal components analysis.

Eco-efficiency has emerged as a management response to waste issues associated with current production processes. Despite the popularity of the term in both business and government circles, limited attention has been paid to measuring and reporting eco-efficiency to government policy makers. Aggregate measures of eco-efficiency are needed, to complement existing measures and to help highlight important patterns in eco-efficiency data. This paper aims to develop aggregate measures of eco-efficiency for use by policy makers. Specifically, this paper provides a unique analysis by applying principal components analysis (PCA) to eco-efficiency indicators in New Zealand. The study reveals that New Zealand's overall eco-efficiency improved for two out of the five aggregate measures over the period 1994/1995-1997/1998. The worsening of the other aggregate measures reflects, among other things, the relatively poor performance of the primary production and related processing sectors. These results show PCA is an effective approach for aggregating eco-efficiency indicators and assisting decision makers by reducing redundancy in an eco-efficiency indicators matrix.

Conservation of Natural Resources↗
PubMedDate not supplied

Comparison of multidimensional scaling and principal component analysis of interspecific variation in bacteria.

Multidimensional scaling (MDS) and principal component analysis (PCA) were applied to bacterial taxonomy. The biochemical profiles of 42 isolates consisting of four species of Enterobacteriaceae were used. Both MDS and PCA use proximity measures such as the correlation coefficient or Euclidean distance to generate a spatial configuration (map) of points in multidimensional space where distances between points reflect the similarity among isolates. Multidimensional scaling and principal component analysis were able to discriminate organisms in two dimensions. The test components of the MDS and PCA factors (derived variables composed of linear combination of biochemical tests) were different for a two-dimensional solution.

Enterobacter↗
PubMedDate not supplied

A guide for applying principal-components analysis and confirmatory factor analysis to quantitative electroencephalogram data.

Principal-components analysis (PCA) has been used in quantitative electroencephalogram (qEEG) research to statistically reduce the dimensionality of the original qEEG measures to a smaller set of theoretically meaningful component variables. However, PCAs involving qEEG have frequently been performed with small sample sizes, producing solutions that are highly unstable. Moreover, solutions have not been independently confirmed using an independent sample and the more rigorous confirmatory factor analysis (CFA) procedure. This paper was intended to illustrate, by way of example, the process of applying PCA and CFA to qEEG data. Explicit decision rules pertaining to the application of PCA and CFA to qEEG are discussed. In the first of two experiments, PCAs were performed on qEEG measures collected from 102 healthy individuals as they performed an auditory continuous performance task. Component solutions were then validated in an independent sample of 106 healthy individuals using the CFA procedure. The results of this experiment confirmed the validity of an oblique, seven component solution. Measures of internal consistency and test-retest reliability for the seven component solution were high. These results support the use of qEEG data as a stable and valid measure of neurophysiological functioning. As measures of these neurophysiological processes are easily derived, they may prove useful in discriminating between and among clinical (neurological) and control populations. Future research directions are highlighted.

Adolescent↗
PubMed2004

Application of time series analysis on molecular dynamics simulations of proteins: a study of different conformational spaces by principal component analysis.

Time series analysis is applied on the collective coordinates obtained from principal component analysis of independent molecular dynamics simulations of alpha-amylase inhibitor tendamistat and immunity protein of colicin E7 based on the Calpha coordinates history. Even though the principal component directions obtained for each run are considerably different, the dynamics information obtained from these runs are surprisingly similar in terms of time series models and parameters. There are two main differences in the dynamics of the two proteins: the higher density of low frequencies and the larger step sizes for the interminima motions of colicin E7 than those of alpha-amylase inhibitor, which may be attributed to the higher number of residues of colicin E7 and/or the structural differences of the two proteins. The cumulative density function of the low frequencies in each run conforms to the expectations from the normal mode analysis. When different runs of alpha-amylase inhibitor are projected on the same set of eigenvectors, it is found that principal components obtained from a certain conformational region of a protein has a moderate explanation power in other conformational regions and the local minima are similar to a certain extent, while the height of the energy barriers in between the minima significantly change. As a final remark, time series analysis tools are further exploited in this study with the motive of explaining the equilibrium fluctuations of proteins.

Amino Acid Sequence↗
PubMed2000

Neuropathological heterogeneity in Alzheimer's disease: a study of 80 cases using principal components analysis.

Three hypotheses have been proposed to explain neuropathological heterogeneity in Alzheimer's disease (AD): the presence of distinct subtypes ('subtype hypothesis'), variation in the stage of the disease ('phase hypothesis') and variation in the origin and progression of the disease ('compensation hypothesis'). To test these hypotheses, variation in the distribution and severity of senile plaques (SP) and neurofibrillary tangles (NFT) was studied in 80 cases of AD using principal components analysis (PCA). Principal components analysis using the cases as variables (Q-type analysis) suggested that individual differences between patients were continuously distributed rather than the cases being clustered into distinct subtypes. In addition, PCA using the abundances of SP and NFT as variables (R-type analysis) suggested that variations in the presence and abundance of lesions in the frontal and occipital lobes, the cingulate gyrus and the posterior parahippocampal gyrus were the most important sources of heterogeneity consistent with the presence of different stages of the disease. In addition, in a subgroup of patients, individual differences were related to apolipoprotein E (ApoE) genotype, the presence and severity of SP in the frontal and occipital cortex being significantly increased in patients expressing apolipoprotein (Apo)E allele epsilon4. It was concluded that some of the neuropathological heterogeneity in our AD cases may be consistent with the 'phase hypothesis'. A major factor determining this variation in late-onset cases was ApoE genotype with accelerated rates of spread of the pathology in patients expressing allele epsilon4.

Age of Onset↗
PubMed2005

Fungus covered insulator materials studied with laser-induced fluorescence and principal component analysis.

A method combining laser-induced fluorescence and principal component analysis to detect and discriminate between algal and fungal growth on insulator materials has been studied. Eight fungal cultures and four insulator materials have been analyzed. Multivariate classifications were utilized to characterize the insulator material, and fungal growth could readily be distinguished from a clean surface. The results of the principal component analyses make it possible to distinguish between algae infected, fungi infected, and clean silicone rubber materials. The experiments were performed in the laboratory using a fiber-optic fluorosensor that consisted of a nitrogen laser and an optical multi-channel analyzer system.

Equipment Contamination↗