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Results for “Principal Component Analysis”

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PubMed2000

Comparison of two exploratory data analysis methods for fMRI: fuzzy clustering vs. principal component analysis.

Exploratory data-driven methods such as Fuzzy clustering analysis (FCA) and Principal component analysis (PCA) may be considered as hypothesis-generating procedures that are complementary to the hypothesis-led statistical inferential methods in functional magnetic resonance imaging (fMRI). Here, a comparison between FCA and PCA is presented in a systematic fMRI study, with MR data acquired under the null condition, i.e., no activation, with different noise contributions and simulated, varying "activation." The contrast-to-noise (CNR) ratio ranged between 1-10. We found that if fMRI data are corrupted by scanner noise only, FCA and PCA show comparable performance. In the presence of other sources of signal variation (e.g., physiological noise), FCA outperforms PCA in the entire CNR range of interest in fMRI, particularly for low CNR values. The comparison method that we introduced may be used to assess other exploratory approaches such as independent component analysis or neural network-based techniques.

Brain↗

PubMed2005

Principal component analysis of complex multijoint coordinative movements.

Principal components analysis (PCA) has not been very much in vogue within the field of movement coordination even though it is useful to reduce data dimensionality and to reveal underlying data structures. Traditionally, studies of coordination between two joints have predominantly made use of relative phase analyses. This has resulted in the identification of principal constraints that govern the Central Nervous System's organization and the control of coordination patterns. However, relative phase analyses on pairwise joints have some drawbacks because they are not optimal for revealing convergent patterns among multijoint coordination modes and for unraveling generic control strategies. In this paper, we present a method to analyze multijoint coordination based on the properties of PC, more specifically the eigenvalues and eigenvectors of the covariance matrix. The comparison between relative phase analysis and PCA shows that both provide similar and consistent results, underscoring the latter technique's sensitivity to the study of coordination performance. In addition, it provides a method for automatic pattern detection as well as an index of performance for each joint within the context of the global coordination pattern. Finally, the merit of the PCA technique within the context of central pattern generators (CPG) will be discussed.

Adult↗

PubMed1984

Principal-components analysis of prehistoric South Asian crania.

Principal-components analysis is used as an investigative procedure for establishing temporal, spatial and evolutionary-developmental changes in Homo sapiens skeletal specimens from prehistoric sites in South Asia. It is concluded that cranial variables which cluster hunter-gatherers within the sample are related to facial architecture with respect to robusticity and size. Older models presumably reflecting genetic affinities and racial classifications of prehistoric South Asians which were based upon univariate-bivariate statistical analyses are not supported by the results of this principal-components analysis.

Asia, Western↗

PubMed1992

[Classification of congenital superior oblique palsy based on principal component analysis].

An attempt was to classify unilateral congenital superior oblique palsy principal component analysis. Each principal component was calculated by taking a linear combination of an eigenvector of the correlation matrix with a standardized original variable. The variables selected for the analysis were vertical deviation in the nine diagnostic positions of 51 cases measured by a synoptometer. The cumulative contributive percent of principal components showed that 88.5% of the variation were accounted for by the first three principal components. The first principal component accounted for 56.7% of the variation in samples indicating the extent of superior oblique palsy in which vertical deviation increases or decreases proportionately. The second principal component accounted for 20.6% of the variation of samples indicating the extent of the incomitance of vertical deviation with a vertical change of gaze. The third principal component accounted for 11.1% of the variation in the sample indicating the extent of the vertical deviation with a horizontal change of gaze.

Adolescent↗

PubMed1997

Nonlinear principal components analysis of neuronal spike train data.

Many recent approaches to decoding neural spike trains depend critically on the assumption that for low-pass filtered spike trains, the temporal structure is optimally represented by a small number of linear projections onto the data. We therefore tested this assumption of linearity by comparing a linear factor analysis technique (principal components analysis) with a nonlinear neural network based method. It is first shown that the nonlinear technique can reliably identify a neuronally plausible nonlinearity in synthetic spike trains. However, when applied to the outputs from primary visual cortical neurons, this method shows no evidence for significant temporal nonlinearities. The implications of this are discussed.

Action Potentials↗

PubMed1988

Assessment of biological age by principal component analysis.

A method of assessing biological age by the application of principal component analysis is reported. Healthy individuals (462) randomly selected from about 6000 men who had taken a 2-day health examination were studied. Out of the 30 physiological variables examined in routine check-ups, 11 variables were selected as suitable for the assessment of biological age based on the results of factor analysis and the physiological meaning of each test. This variable set was then submitted to principal component analysis, and the 1st principal component obtained from this analysis was used as an equation for assessing one's biological age. However, the biological age calculated from this equation is expressed as a score, so the estimated score was transformed to years (biological age) using the T-score idea. The biological age estimated by this method is practically useful and theoretically valid in contrast with the multiple regression model, because this approach eliminates and overcomes the following 2 big problems of the multiple regression model: (1) the distortion of the individual biological age at the regression edges; and (2) a theoretical contradiction in that a perfect model will merely be predicting the subject's chronological age, not his biological age.

Adult↗

PubMed1982

On-line classification of arterial stenosis severity using principal component analysis applied to Doppler ultrasound signals.

Principal component analysis is a powerful method of feature extraction which can be applied to continuous-wave Doppler waveforms. A microprocessor system for the on-line calculation of the coefficients of principal components has been devised and tested in an experimental model. Doppler waveforms were obtained from positions distal to stenoses of known severity implanted in the iliac arteries of three dogs and classified into one of four groups. By reference to data from a previous series of experiments the microprocessor correctly classified 75% of stenoses. The remaining 25% were all classified as being one group more severe than they actually were.

Animals↗

PubMed2006

High dynamic range image rendering with a Retinex-based adaptive filter.

We propose a new method to render high dynamic range images that models global and local adaptation of the human visual system. Our method is based on the center-surround Retinex model. The novelties of our method is first to use an adaptive filter, whose shape follows the image high-contrast edges, thus reducing halo artifacts common to other methods. Second, only the luminance channel is processed, which is defined by the first component of a principal component analysis. Principal component analysis provides orthogonality between channels and thus reduces the chromatic changes caused by the modification of luminance. We show that our method efficiently renders high dynamic range images and we compare our results with the current state of the art.

Algorithms↗

PubMed2001

Birth as a multidimensional experience: comparison of the English- and German-language versions of Salmon's Item List.

Results concerning satisfaction with the birth experience in different trials are difficult to compare, owing to a lack of internationally used research scales. Salmon's Item List (SIL) is easy-to-handle and would therefore be very helpful for research as well as for obstetric clinic quality control. Two hundred and fifty-one patients were investigated using a German-language version of SIL (SIL-ger); the statistical evaluation was carried out by means of a principal components analysis. Principal components analysis revealed two major findings: (1) as stated by other authors the birth experience is multidimensional, each aspect influencing the others in a non-linear way; (2) in addition to Salmon's dimensions (i.e. postnatal 'fulfillment', intranatal 'physical discomfort' and intranatal 'emotional distress') another postnatal dimension labeled 'negative emotional experience' was detected. Not only are intranatal experiences multidimensional, but so too are evaluative feelings afterwards. In addition to fulfillment, as developed by Salmon, a dimension of negative emotional experience needs to be taken into account. This dimension does not correlate in a linear way with fulfillment. It is appropriate to use SIL in research. Before using it for purposes of clinical quality control, however, larger samples need to be evaluated in order to prove the stability of the factor structure.

Cross-Cultural Comparison↗

PubMed1996

[Fast and accurate numerical method for principal components analysis dealing with large image data sets].

The principal components analysis has been applied to various imaging studies in nuclear medicine. This technical report describes a fast and accurate numerical method of calculating eigenvalues and eigenvectors in the principal components analysis dealing with larger image data sets. The method employs both data transformation and matrix transpose of original data sets to calculate a variance-covariance or correlation matrix. The method was tested on actual image data sets using a common workstation, confirming faster execution time and efficient accuracy in comparison to a standard method.

Image Processing, Computer-Assisted↗

PubMed1999

Spectral quantitation by principal component analysis using complex singular value decomposition.

Principal component analysis (PCA) is a powerful method for quantitative analysis of nuclear magnetic resonance spectral data sets. It has the advantage of being model independent, making it well suited for the analysis of spectra with complicated or unknown line shapes. Previous applications of PCA have required that all spectra in a data set be in phase or have implemented iterative methods to analyze spectra that are not perfectly phased. However, improper phasing or imperfect convergence of the iterative methods has resulted in systematic errors in the estimation of peak areas with PCA. Presented here is a modified method of PCA, which utilizes complex singular value decomposition (SVD) to analyze spectral data sets with any amount of variation in spectral phase. The new method is shown to be completely insensitive to spectral phase. In the presence of noise, PCA with complex SVD yields a lower variation in the estimation of peak area than conventional PCA by a factor of approximately 2. The performance of the method is demonstrated with simulated data and in vivo 31P spectra from human skeletal muscle.

Analysis of Variance↗

PubMed1999

Similarity relations of DNA and RNA polymerases investigated by the principal component analysis of amino acid sequences.

The principal component analysis based on the physicochemical properties of amino acid residues is applied to DNA and RNA polymerases to assign the sequence motifs for the polymerization activities of these proteins. After the reconfirmation of the sequence motifs of families A and B of DNA polymerases indicated previously, it elucidates the sequence motifs for the polymerization activity of DNA polymerase III (family C) by the similarity to the polymerization center of multimeric DNA dependent RNA polymerases. This identification proceeds to clarify the sequence motifs for polymerization activities of primases; eukaryotic and archaebacterial primases carry motifs similar to those of family C, while the motifs of eubacterial primase fall into the category of the motifs in family B DNA polymerases such as alpha, delta, epsilon and II. This finding means that DNA dependent RNA polymerases are also divided into groups corresponding to three families, A, B and C, because the monomeric DNA dependent RNA polymerases in phages are reconfirmed to carry sequence motifs similar to those of family A DNA polymerases. Furthermore, the three families of polymerization motifs are found to fall within the variation range of polymerization motifs displayed by many RNA dependent RNA polymerases, suggesting a close evolutionary relation between them. The sequence motifs for polymerization activities of reverse transcriptase and telomerase seem to be the intermediate between family A DNA polymerase and some RNA dependent RNA polymerases, e.g., from Leviviridae. On the contrary, the sequence fragments similar to the nucleotidyltransferase superfamily including DNA polymerase beta are not found in any RNA dependent RNA polymerase, suggesting their other lineage of polymerization motifs.

Amino Acid Sequence↗

PubMed1986

New indices for thyroid functional status, hormone binding, and peripheral hormone metabolism. Principal component analysis of 24,000 clinical data.

Principal component analysis of three thyroid function tests, thyroxine (T4), 3,5,3'-triiodothyronine (T3), and T3 uptake (T3U), was done using 24,000 data obtained from patients with a wide range of pathophysiologic conditions related to the thyroid. The three component scores were obtained as follows: Z1 = 2.62 square root T4 + 0.63 square root T3 + 3.18 square root T3U - 32.43; Z2 = 0.91 square root T4 + 0.24 square root T3 - 4.68 square root T3U + 20.14; and Z3 = 3.94 square root T4 + 0.95 square root T3 + 0.18 square root T3U - 1.53 (T4 micrograms/dL, T3 ng/dL, T3U%). The first component (Z1) represents an apparent axis to the direction of thyroid functional status. It provides a new metabolic index putting conventional free T4 and free T3 indices together. The second component (Z2) was found to be a sensitive indicator of abnormal hormone binding. It showed a close correlation with serum concentration of thyroxine-binding globulin. The third component (Z3) represents the degree of T3 predominance over T4. Computation of these scores will facilitate the diagnosis of atypical cases in which hyper-or hypothyroidism is complicated by abnormal peripheral hormone binding and/or metabolism.

Humans↗

PubMed2005

Functional principal component analysis of fMRI data.

We describe a principal component analysis (PCA) method for functional magnetic resonance imaging (fMRI) data based on functional data analysis, an advanced nonparametric approach. The data delivered by the fMRI scans are viewed as continuous functions of time sampled at the interscan interval and subject to observational noise, and are used accordingly to estimate an image in which smooth functions replace the voxels. The techniques of functional data analysis are used to carry out PCA directly on these functions. We show that functional PCA is more effective than is its ordinary counterpart in recovering the signal of interest, even if limited or no prior knowledge of the form of hemodynamic function or the structure of the experimental design is specified. We discuss the rationale and advantages of the proposed approach relative to other exploratory methods, such as clustering or independent component analysis, as well as the differences from methods based on expanded design matrices.

Adult↗

PubMed2006

A novel incremental principal component analysis and its application for face recognition.

Principal component analysis (PCA) has been proven to be an efficient method in pattern recognition and image analysis. Recently, PCA has been extensively employed for face-recognition algorithms, such as eigenface and fisherface. The encouraging results have been reported and discussed in the literature. Many PCA-based face-recognition systems have also been developed in the last decade. However, existing PCA-based face-recognition systems are hard to scale up because of the computational cost and memory-requirement burden. To overcome this limitation, an incremental approach is usually adopted. Incremental PCA (IPCA) methods have been studied for many years in the machine-learning community. The major limitation of existing IPCA methods is that there is no guarantee on the approximation error. In view of this limitation, this paper proposes a new IPCA method based on the idea of a singular value decomposition (SVD) updating algorithm, namely an SVD updating-based IPCA (SVDU-IPCA) algorithm. In the proposed SVDU-IPCA algorithm, we have mathematically proved that the approximation error is bounded. A complexity analysis on the proposed method is also presented. Another characteristic of the proposed SVDU-IPCA algorithm is that it can be easily extended to a kernel version. The proposed method has been evaluated using available public databases, namely FERET, AR, and Yale B, and applied to existing face-recognition algorithms. Experimental results show that the difference of the average recognition accuracy between the proposed incremental method and the batch-mode method is less than 1%. This implies that the proposed SVDU-IPCA method gives a close approximation to the batch-mode PCA method.

Algorithms↗

PubMed1990

[Multivariate analysis for the study of craniofacial structure. Selection of parameters on using the principal component analysis].

The present study was undertaken to discuss the effects of parameters on using the principal component analysis for the evaluation of the craniofacial structures. Materials consisted of lateral roentgenocephalograms of 100 adult Japanese females. They were divided into three groups by ANB angle. Linear and angular measurements were selected as parameters to apply the principal component analysis, and scattergrams made by the first and second principal component were compared. The results of the analysis were varied by the parameters. Especially there was a great difference between the real size and the value corrected by isometric method on scattergrams made by the first and second components. Results indicated the importance of using appropriate parameters for the research purpose.

Adult↗

PubMed1993

Principal component analysis learning algorithms: a neurobiological analysis.

The biological relevance of principal component analysis (PCA) learning algorithms is addressed by: (i) describing a plausible biological mechanism which accounts for the changes in synaptic efficacy implicit in Oja's 'Subspace' algorithm (Int. J. neural Syst. 1, 61 (1989)); and (ii) establishing a potential role for PCA-like mechanisms in the development of functional segregation. PCA learning algorithms comprise an associative Hebbian term and a decay term which interact to find the principal patterns of correlations in the inputs shared by a group of units. We propose that the presynaptic component of this decay could be regulated by retrograde signals that are translocated from the terminal arbors of presynaptic neurons to their cell bodies. This proposal is based on reported studies of structural plasticity in the nervous system. By using simulations we demonstrate that PCA-like mechanisms can eliminate afferent connections whose signals are unrelated to the prevalent pattern of afferent activity. This elimination may be instrumental in refining extrinsic cortico-cortical connections that underlie functional segregation.

Algorithms↗

PubMed2002

Block principal component analysis with application to gene microarray data classification.

We propose a block principal component analysis method for extracting information from a database with a large number of variables and a relatively small number of subjects, such as a microarray gene expression database. This new procedure has the advantage of computational simplicity, and theory and numerical results demonstrate it to be as efficient as the ordinary principal component analysis when used for dimension reduction, variable selection and data visualization and classification. The method is illustrated with the well-known National Cancer Institute database of 60 human cancer cell lines data (NCI60) of gene microarray expressions, in the context of classification of cancer cell lines.

DNA, Neoplasm↗