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At least 289 records · Page 16Linked to original sources
PubMed2003

Event-related potentials in newborns with and without familial risk for dyslexia: principal component analysis reveals differences between the groups.

Differences revealed by factor scores extracted by principal component analysis (PCA) from event-related potential (ERP) data of newborns with and without familial risk for dyslexia were examined and compared to results obtained by using original averaged ERPs. ERPs to consonant-vowel syllables (synthetic /ba/, /da/, /ga/; and natural /paa/, /taa/, /kaa/) were recorded from 26 at-risk and 23 control 1-7 day-old infants. The stimuli were presented equiprobably and with interstimulus intervals varying at random from 3,910 to 7,285 ms. Statistically significant between-group differences were found to be relatively similar irrespective of the methods of analysis (original ERPs vs. factor scores from PCA). Responses to /ga/ differed from those to /ba/ and /da/ between the groups in the right hemisphere at the latencies of 50-170 ms (Factor 4) and 540-630 ms (Factor 3). The groups differed also in their responses to /da/ in the posterior electrode sites at 740-940 ms (Factor 2). There were no group differences in the natural stimulus set. These results demonstrate that brain activation differences may be implicated in risk for dyslexia immediately after birth. The results also show that the PCA-ANOVA procedure is an effective way of identifying the group-related variance in the ERP-data when the component structure, such as those of infants, is not well-known in advance.

Acoustic Stimulation↗
PubMed1996

Classification of astrocytomas and malignant astrocytomas by principal components analysis and a neural net.

The classification of astrocytomas, astrocytomas with anaplastic foci and glioblastoma multiformes is not always straightforward because the tumors form a histological continuum. The use of principal component analysis (PCA) and neural nets in the classification of these tumors is explored. PCA was performed on 14 histological features recorded from 52 gliomas classified by the Radiation Therapy Oncology Group method (17 astrocytomas, 18 astrocytomas with anaplastic foci, 17 glioblastoma multiformes). Four of the 14 possible 'scores' derived from this analysis were selected to summarize the histological variability seen in all the tumors. These scores were mostly significantly different between tumor types and were thus used to successfully train a neural net to correctly classify these tumors. The first principal component (score) supported the use of increasing cellularity, mitoses, endothelial proliferation, and necrosis in differentiating between the tumor categories, but accounted for only 39% of the variability seen. Other histological features that were significant components of the other scores included the presence of multinucleated or giant cells, gemistocytes, atypical mitoses and changes in nuclear chromatin. Computer programs derived from the methodology described provide a way of standardizing glioma diagnosis and may be extended to assist with management decisions.

Astrocytoma↗
PubMedDate not supplied

Generalized 2D principal component analysis for face image representation and recognition.

In the tasks of image representation, recognition and retrieval, a 2D image is usually transformed into a 1D long vector and modelled as a point in a high-dimensional vector space. This vector-space model brings up much convenience and many advantages. However, it also leads to some problems such as the Curse of Dimensionality dilemma and Small Sample Size problem, and thus produces us a series of challenges, for example, how to deal with the problem of numerical instability in image recognition, how to improve the accuracy and meantime to lower down the computational complexity and storage requirement in image retrieval, and how to enhance the image quality and meanwhile to reduce the transmission time in image transmission, etc. In this paper, these problems are solved, to some extent, by the proposed Generalized 2D Principal Component Analysis (G2DPCA). G2DPCA overcomes the limitations of the recently proposed 2DPCA (Yang et al., 2004) from the following aspects: (1) the essence of 2DPCA is clarified and the theoretical proof why 2DPCA is better than Principal Component Analysis (PCA) is given; (2) 2DPCA often needs much more coefficients than PCA in representing an image. In this work, a Bilateral-projection-based 2DPCA (B2DPCA) is proposed to remedy this drawback; (3) a Kernel-based 2DPCA (K2DPCA) scheme is developed and the relationship between K2DPCA and KPCA (Scholkopf et al., 1998) is explored. Experimental results in face image representation and recognition show the excellent performance of G2DPCA.

Algorithms↗
PubMed1993

[Internal validation of a measurement scale: relation between principal component analysis, Cronbach's alpha coefficient and intra-class correlation coefficient].

The objective is to establish a simple relationship between two frequently used validation techniques which have been developed in the literature along the same lines: Principal Component Analysis and Cronbach's alpha. We have shown that under certain conditions, it is possible to estimate the reliability by using the results of a Principal Component Analysis only. Moreover, we report the relation between Cronbach's alpha and intraclass correlation coefficient, which are both used to estimate the reliability of continuous measures.

Data Interpretation, Statistical↗
PubMed2005

Qualitative study of ethanol content in tequilas by Raman spectroscopy and principal component analysis.

Using Raman spectroscopy, with an excitation radiation source of 514.5 nm, and principal component analysis (PCA) was elaborated a method to study qualitatively the ethanol content in tequila samples. This method is based in the OH region profile (water) of the Raman spectra. Also, this method, using the fluorescence background of the Raman spectra, can be used to distinguish silver tequila from aged tequilas. The first three PCs of the Raman spectra, that provide the 99% of the total variance of the data set, were used for the samples classification. The PCA1 and PCA2 are related with the water (or ethanol) content of the sample, whereas the PCA3 is related with the fluorescence background of the Raman spectra.

Alcoholic Beverages↗
PubMed2002

Two new spectrophotometric approaches to the multicomponent analysis of the acetaminophen and caffeine in tablets by classical least-squares and principal component regression techniques.

Classical least-squares (CLS) and principal component regression (PCR) techniques were proposed for the simultaneous analysis of tablets containing acetaminophen and caffeine without using a chemical separation procedure. The chemometric calibrations were prepared by measuring the absorbances values at the 15 wavelengths in the spectral region 215-285 nm and by using a training set of the mixtures of both drugs in 0.1 M HCI. The obtained chemometric calibrations were used for the estimation of acetaminophen and caffeine in samples. The numerical calculations were performed with the 'MAPLE V' software. By applying two techniques to synthetic mixtures, the mean recoveries and the relative standard deviations in the CLS and PCR techniques were found as 99.5 and 1.29, 99.7 and 1.00% for acetaminophen and 99.9 and 1.92, 100.0 and 1.178% for caffeine, respectively. Our results were compared with those obtained previously by one of us considering HPLC method as a reference method. These two methods were successfully applied to a pharmaceutical tablet formulation of two drugs.

Acetaminophen↗
PubMed2005

Improved background removal method using principal components analysis for spatially resolved electron energy loss spectroscopy.

Principal components analysis (PCA) factor filtering is implemented for the improvement of background removal in noisy spectra. When PCA is used as a method for filtering before background removal in electron energy loss spectroscopy elemental maps, an improvement in the accuracy of the background fit with very short fitting intervals is achieved, leading to improved quality of elemental maps from noisy spectra. This opens the possibility to use shorter exposure times for elemental mapping, leading to fewer problems with, for example, drift and beam damage.

Journal Article↗
PubMedDate not supplied

Geographic variability of digital ridge-counts: principal components analysis of male and female world samples.

A principal components analysis was made on the means of the ten digital ridge-counts, separately for 195 male populations and 165 female populations, using the variance/covariance matrix. The first three components explain respectively 74%, 12% and 4.6% in the male sample and 77.2%, 11.3% and 4.1% in the female sample. The factor scores of the individual samples were computed and scatter diagrams were plotted for the first two components. They show the existence of a geographic distribution. Ellipses of dispersion of the geographic groups show some similarity between male and female samples, although homogeneity is less than in females and less on the right than on the left hand. The structure of the components shows that the first one corresponds to a factor size, the second contrasts the thumb with fingers 2 and 3, third opposes fingers 2 and 3 to fingers 4 and 5. Data from more recent population studies can easily be referred to the suggested scheme. Their position will define more accurately the differentiation between the geographic groups.

Africa↗
PubMedDate not supplied

Principal components of finger ridge-counts: their universality.

A principal component analysis was carried out on radial and ulnar finger ridge-count data on a sample of fishermen of the sea coast of Puri in the state of Orissa in India. The component structure is very similar to that obtained earlier by Roberts and Coope for some English populations, by Arrieta and Lostao for a Basque population, by Siervogel et al. for a White American population, by Jantz and Hawkinson, and Jantz et al. for American and African populations, and by other authors for other populations. The initial components are bilaterally symmetric and the structure of these components is the same whether the two sides are taken separately or together. Only the latter components represent a certain amount of bilateral asymmetry. The first component is a 'size' component, indicating total finger ridge-count; the second component is a radial-ulnar contrast. From a comparison with previous studies on other populations, it appears that the component structure corresponding to the larger eigenvalues is fairly universal; there is a certain lack of universality in the structure of the components corresponding to smaller eigenvalues as well as in the order of these components, especially the rotated ones, when the corresponding eigenvalues are very close. As observed by previous authors, components corresponding to larger eigenvalues do not necessarily exhibit larger inter-population differences. However, there is lack of universality in the order of the components and in the structure of the components that exhibit large inter-population differences.

Dermatoglyphics↗
PubMed1985

Learning disabilities and intelligence test results: a model based on a principal components analysis of the WISC-R.

The unrotated principal components analysis of the WISC-R normative data yields a bipolar factor 2 which corresponds to a verbal-non-verbal continuum of test material. A review of published WISC-R and WISC data from learning disabled (LD) children reveals that the amount of deficit shown by these children on any particular subtest is closely proportional to the degree of verbal content, as expressed by the factor 2 score coefficient of that subtest. This test-specific effect was found to be very much greater in LD boys than in LD girls.

Adolescent↗
PubMed2004

Asymptotic biases of the unrotated/rotated solutions in principal component analysis.

Asymptotic biases of the parameter estimates in principal component analysis with substantial misspecification are derived. The solutions for unstandardized and standardized observed variables are considered with and without orthogonal and oblique rotations. The distribution of observed variables can be non-normal as long as the finite fourth-order moments of the observed variables exist. When multivariate normality holds for the observed variables, substantial reduction of the amount of computation can be achieved. Numerical examples with simulations are given, with some discussion on the tendency of the biases to reduce the absolute values of parameter estimates.

Bias↗
PubMed2004

Maximum likelihood principal components regression on wavelet-compressed data.

Maximum likelihood principal component regression (MLPCR) is an errors-in-variables method used to accommodate measurement error information when building multivariate calibration models. A hindrance of MLPCR has been the substantial demand on computational resources sometimes made by the algorithm, especially for certain types of error structures. Operations on these large matrices are memory intensive and time consuming, especially when techniques such as cross-validation are used. This work describes the use of wavelet transforms (WT) as a data compression method for MLPCR. It is shown that the error covariance matrix in the wavelet and spectral domains are related through a two-dimensional WT. This allows the user to account for any effects of the wavelet transform on spectral and error structures. The wavelet transform can be applied to MLPCR when using either the full error covariance matrix or the smaller pooled error covariance matrix. Simulated and experimental near-infrared data sets are used to demonstrate the benefits of using wavelets with the MLPCR algorithm. In all cases, significant compression can be obtained while maintaining favorable predictive ability. Considerable time savings were also attained, with improvements ranging from a factor of 2 to a factor of 720. Using the WT-compressed data in MLPCR gave a reduction in prediction errors compared to using the raw data in MLPCR. An analogous reduction in prediction errors was not always seen when using PCR.

Journal Article↗
PubMed2005

Principal-components analysis of fluorescence cross-section spectra from pathogenic and simulant bacteria.

Principal-components analysis of a new set of highly resolved (< 1 nm) fluorescence cross-section spectra excited at 354.7 nm over the 370-646 nm band has been used to demonstrate the potential ability of UV standoff lidars to discriminate among particular biological warfare agents and simulants over short ranges. The remapped spectra produced by this technique from Bacillus globigii (Bg) and Bacillus anthracis (Ba) spores were sufficiently different to allow them to be cleanly separated, and the Ba spectra obtained from Sterne and Ames strain spores were distinguishable. These patterns persisted as the spectral resolution was subsequently degraded in processing from approximately 1 to 34 nm. This is to the author's knowledge the first time that resolved fluorescence spectra from biological warfare agents have been speciated or shown to be distinguishably different from those normally used surrogates by optical spectroscopy.

Algorithms↗
PubMed2000

Evolving faces from principal components.

A system that uses an underlying genetic algorithm to evolve faces in response to user selection is described. The descriptions of faces used by the system are derived from a statistical analysis of a set of faces. The faces used for generation are transformed to an average shape by defining locations around each face and morphing. The shape-free images and shape vectors are then separately subjected to principal components analysis. Novel faces are generated by recombining the image components (eigenfaces) and then morphing their shape according to the principal components of the shape vectors (eigenshapes). The prototype system indicates that such a statistical analysis of a set of faces can produce plausible, randomly generated photographic images.

Biological Evolution↗
PubMed1981

Principal components analysis of sources of variability in retinal ganglion cell responses.

An approach to the functional organization of retinal ganglion cell processing in terms of correlation matrices (Levine and Shefner, 1975; 1977a, b; Shefner and Levine, 1979) is extended by applying Principal Components Analysis. This analysis reduces each correlation matrix to a few components which are implicit in the data. The component loadings describe properties of the system in terms of loadings (correlations) of time bins on the underlying components. Each component is identified by the experimental conditions associated with the highest loadings. Mixed conditions are quantitatively interpreted as weighted contributions from the various identified components. This has led to new interpretations of existing data. Several properties of ganglion cell inputs are analyzed in this manner, including ON and OFF processes, center and surround mechanisms, rod and cone inputs, and spatially distinct areas within the receptive field center. Although the details vary, generally one of the components is highly associated with ON processes and the other with OFF and/or MAINTAINED processes. several advantages may be realized through the use of Principal Components Analysis: (1) all of the data contribute to the analysis, (2) the number and relative importance of contributing processes may be assessed, (3) the relative contribution of underlying processes to mixed responses may be assessed, and (4) the most parsimonious representation of the data is obtained.

Animals↗
PubMed1985

Principal component analysis for a better understanding of the herbicidal effectivity of some benzonitriles.

To gain more insight in mode of action of ten different 4-hydroxy-benzonitrile derivatives, their biological activities in eight bioassays, and their lipophilicity and adsorptivity determined by thin-layer chromatography in nine different systems were subjected to principal component analysis. Four background components explained about 90% of total variance. Only three of eight biological activities, the inhibition of the 2,6-dichlorophenol-indophenol reduction by spinach and wheat chloroplasts and the CO2 fixation of wheat seedlings had not any common background components with the physico-chemical parameters of the compounds. The nonlinear mapping of principal component loadings and variables showed, that the in vivo and in vitro biological activities differed considerably and depended on the object investigated. The effectivity of compounds is governed mainly by the number of substituents and by the presence of free hydroxy group.

Chromatography, High Pressure Liquid↗
PubMed2001

Evaluation of the effect of data pre-treatment procedures on classical pattern recognition and principal components analysis: a case study for the geographical classification of tea.

A simple transformation that uses the half-range and central value has been used as a data pre-treatment procedure for principal component analysis (PCA) and pattern recognition techniques. The results obtained have been compared with the results from classical normalisation of data (mean normalisation, maximum normalisation and range normalisation), autoscaling and the minimum-maximum transformation. Three data sets were used in the study. The first was formed by determining 17 elements in 53 tea samples (901 pieces of data). The second and third data sets arose from two long-term drift studies performed to examine instrumental stability at standard and robust conditions. The instruments used were an inductively coupled plasma atomic emission spectrometer and an inductively coupled plasma mass spectrometer. Each drift diagnosis experiment consisted of replicate determinations of a test solution containing 15 analytes at 10 mg l-1 over 8 h without recalibration. Twenty-nine emission lines were determined 99 times, thus, each data set was formed by 2881 pieces of data. Data pre-treatment was applied to the three data sets prior to the use of principal component analysis, cluster analysis, linear discrimination analysis and soft independent modelling of class analogy. The study revealed that the half-range and central value transformation resulted in a better classification of the tea samples than that achieved using the classical normalisation. The loadings in the PCA for the long-term stability study, under both standard and robust conditions, were found to be similar to the drift trends only when the minimum-maximum transformation and the mean or maximum normalizations were used as data pre-treatments.

Humans↗
PubMed1999

Principal components scores and loadings plots for visualisation of the electrospray ionisation liquid chromatography mass spectra of a mixture of chlorophyll degradation products at different cone voltages.

Positive ion electrospray liquid chromatography mass spectrometry was performed on a mixture of allomers obtained from the degradation of chlorophyll a, at three cone voltages. An approach is described for obtaining principal components loadings and scores plots, involving mass selection, normalisation and standardisation of the data, principal components analysis and three dimensional projections. The loadings plots group ions which are assigned to five major compounds in the mixture by reference to the scores. At higher cone voltage fragmentation and differentiation between compounds with identical molecular weights is observed.

Chlorophyll↗