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Cranial capacity/cranial base relationships and prediction of vault form: a canonical correlation analysis.

Canonical correlation analysis was used to test an hypothesized morphological relationship between vault form and cranial capacity relative to length of the chondrocranium. Ninety-five adult male Czech skulls were measured for vault form expressed as length, width and height of the brain case; the chondrocranium was represented by nasion-basion and basion-opisthion lengths. In terms of explained variation, the first and most important dimension of covariation between vault and chondrocranial variables was size. The second most significant dimension of covariation expressed the hypothesized shape relationships--i.e., overall size being equal, the shorter the chondrocranial base relative to cranial capacity, the shorter and wider the vault. Furthermore, the competing hypothesis that vault form is determined by facial length proved untenable since facial length was predictive of vault shape only when measured as prosthion-basion, a measure that incorporates basal length. When corrected for basal length, facial length is unrelated to vault form. The results are consistent with the assumption that phylogenetic and microevolutionary trends toward brachycephaly in man stem from changes in the relationship between two components of skull growth, the chondrocranial base and the brain.

Adult↗

Research methodology. Part IV: Understanding canonical correlation analysis.

Canonical correlation is presented as a technique to determine how sets of dependent variables are related with sets of independent variables. Canonical correlation reveals the strength of the relationship between the clusters using case data as illustration, three pairs of clusters (factors or profiles) emerged. Interpretation of the clusters are presented. As indicated in the case presentation, Canonical Correlation (CA) is the fourth in a series of methodologies selected for illustration as precursors to advanced statistics and modeling. In this paper, background will be given, a schematic example presented, sample size and CA, SPSS procedure to perform CA, and interpretation of CA and possible uses of CA in nursing research.

Analysis of Variance↗

Personality disorders in substance abusers: validation of the DIP-Q through principal components factor analysis and canonical correlation analysis.

BACKGROUND: Personality disorders are common in substance abusers. Self-report questionnaires that can aid in the assessment of personality disorders are commonly used in assessment, but are rarely validated. METHODS: The Danish DIP-Q as a measure of co-morbid personality disorders in substance abusers was validated through principal components factor analysis and canonical correlation analysis. A 4 components structure was constructed based on 238 protocols, representing antagonism, neuroticism, introversion and conscientiousness. The structure was compared with (a) a 4-factor solution from the DIP-Q in a sample of Swedish drug and alcohol abusers (N = 133), and (b) a consensus 4-components solution based on a meta-analysis of published correlation matrices of dimensional personality disorder scales. RESULTS: It was found that the 4-factor model of personality was congruent across the Danish and Swedish samples, and showed good congruence with the consensus model. A canonical correlation analysis was conducted on a subset of the Danish sample with staff ratings of pathology. Three factors that correlated highly between the two variable sets were found. These variables were highly similar to the three first factors from the principal components analysis, antagonism, neuroticism and introversion. CONCLUSION: The findings support the validity of the DIP-Q as a measure of DSM-IV personality disorders in substance abusers.

Adult↗

Nonlinear canonical correlation analysis by neural networks.

Canonical correlation analysis (CCA) is widely used to extract the correlated patterns between two sets of variables. A nonlinear canonical correlation analysis (NLCCA) method is formulated here using three feedforward neural networks. The first network has a double-barreled architecture, and an unconventional cost function, which maximizes the correlation between the two output neurons (the canonical variates). The remaining two networks map from the canonical variates back to the original two sets of variables. Tested on data sets with correlated nonlinear structures, NLCCA showed that the underlying nonlinear structures could be retrieved accurately under moderately noisy conditions. After a mode had been retrieved, NLCCA was applied to the residual to successfully retrieve the next mode. When tested for prediction skills, the NLCCA outperformed the CCA when the two sets of variables contained correlated nonlinear structures.

Artifacts↗

A learning algorithm for adaptive canonical correlation analysis of several data sets.

Canonical correlation analysis (CCA) is a classical tool in statistical analysis to find the projections that maximize the correlation between two data sets. In this work we propose a generalization of CCA to several data sets, which is shown to be equivalent to the classical maximum variance (MAXVAR) generalization proposed by Kettenring. The reformulation of this generalization as a set of coupled least squares regression problems is exploited to develop a neural structure for CCA. In particular, the proposed CCA model is a two layer feedforward neural network with lateral connections in the output layer to achieve the simultaneous extraction of all the CCA eigenvectors through deflation. The CCA neural model is trained using a recursive least squares (RLS) algorithm. Finally, the convergence of the proposed learning rule is proved by means of stochastic approximation techniques and their performance is analyzed through simulations.

Algorithms↗

[A canonical correlation analysis on the traffic conditions and the road injury in China in 1996].

In order to expound the impact of traffic condition to road injury, the relations of road injury with motorization, road situation e and highway capacity were studied, using Canonical Correlation analysis method. Results showed that the Canonical Correlation Coefficient A(0.9486) was representing the direct correlation of the motorization with the injury and death rates, referring the higher the motorization, the greater the death and injury rates. The Canonical Correlation Coefficient B(0.9220) indicated the direct correlation between the highway capacity and the frequency of road injury, as well as the larger the highway capacity, the more frequency of road injury occurrance. The Canonical Correlation Coefficient C(0.6446) revealed the relations between levels of road situation and the accident rates per 10,000 vehicles. Results showed that the higher the quality of roadway, the lower was the accident rate. In view of this, traffic condition was closely related to road injury. Thus, the frequency of accident, death rate and injury rate could be reduced through improving the road quality and traffic conditions. Relations between two sets of random variables, the ties of the canonical variables and the original variables could all be well analysed with Canonical Correlation analysis. Canonical Correlation is useful to analyse two sets of random variables in epidemiological studies since it provides a great amount of information to be applied and operated widely and easily.

Accidents, Traffic↗

Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs.

Canonical correlation analysis (CCA) is applied to analyze the frequency components of steady-state visual evoked potentials (SSVEP) in electroencephalogram (EEG). The essence of this method is to extract a narrowband frequency component of SSVEP in EEG. A recognition approach is proposed based on the extracted frequency features for an SSVEP-based brain computer interface (BCI). Recognition Results of the approach were higher than those using a widely used fast Fourier transform (FFT)-based spectrum estimation method.

Algorithms↗

Tissue segmentation and classification of MRSI data using canonical correlation analysis.

In this article an accurate and efficient technique for tissue typing is presented. The proposed technique is based on Canonical Correlation Analysis, a statistical method able to simultaneously exploit the spectral and spatial information characterizing the Magnetic Resonance Spectroscopic Imaging (MRSI) data. Recently, Canonical Correlation Analysis has been successfully applied to other types of biomedical data, such as functional MRI data. Here, Canonical Correlation Analysis is adapted for MRSI data processing in order to retrieve in an accurate and efficient way the possible tissue types that characterize the organ under investigation. The potential and limitations of the new technique have been investigated by using simulated as well as in vivo prostate MRSI data, and extensive studies demonstrate a high accuracy, robustness, and efficiency. Moreover, the performance of Canonical Correlation Analysis has been compared to that of ordinary correlation analysis. The test results show that Canonical Correlation Analysis performs best in terms of accuracy and robustness.

Algorithms↗

A neural implementation of canonical correlation analysis.

We derive a new method of performing Canonical Correlation Analysis with Artificial Neural Networks. We demonstrate the network's capabilities on artificial data and then compare its effectiveness with that of a standard statistical method on real data. We demonstrate the capabilities of the network in two situations where standard statistical techniques are not effective: where we have correlations stretching over three data sets and where the maximum nonlinear correlation is greater than any linear correlation. The network is also applied to Becker's (Network: Computation in Neural Systems, 1996, 7:7-31) random dot stereogram data and shown to be extremely effective at detecting shift information.

Journal Article↗

Fast active appearance model search using canonical correlation analysis.

A fast AAM search algorithm based on canonical correlation analysis (CCA-AAM) is introduced. It efficiently models the dependency between texture residuals and model parameters during search. Experiments show that CCA-AAMs, while requiring similar implementation effort, consistently outperform standard search with regard to convergence speed by a factor of four.

Algorithms↗

Novel nonparametric approach to canonical correlation analysis with applications to low CNR functional MRI data.

Detection of activation in functional MRI (fMRI) is often complicated by the low contrast-to-noise ratio (CNR) in the data. The primary source of the difficulty is the fact that for activities that are subtle the signal can be hidden inside the inherent noise in the data. Classical univariate methods based on t-test or F-test are susceptible to noise, as they fail to harness systematic correlations in evoked responses within neighboring voxels. Here the power of a multivariate statistical analysis tool known as canonical correlation analysis (CCA) in fMRI studies is demonstrated where the CNR is low. As a further illustration of the power of the method, a comparative study of CCA and ordinary correlation analysis using simulated data under various noise levels is also performed. A novel nonparametric approach is introduced to calculate the P-values from the distribution of the complicated test statistic. The circumstances under which CCA is a better performer as well as when it is not the case are discussed. As an example, this method is applied to detect hippocampal activation from memory-related tasks.

Adult↗

Canonical correlation analysis of risk factors and clinical outcomes in cardiac surgery.

Assessment of the association between risk factors and outcomes in cardiac surgery is a complex problem. The aim of this study was to explore the relationship between possible risk factors and several clinical outcomes in cardiac surgery by using canonical correlation analysis (CCA). This retrospective study of 2605 consecutive adult patients who underwent cardiac surgery, evaluated 74 potential risk factors and up to 12 outcomes by canonical correlation analysis. For three serious outcomes, sternal wound complications/mediastinitis, cerebral complications, and perioperative myocardial infarctions, CCA was preceded by univariate analyses and backward stepwise multivariate logistic regression analyses. The CCA suggests that the major risk factors for complications in these models are intraoperative and postoperative risk factors. The power of risk prediction models developed with multivariate regression analysis can be enhanced by application of canonical correlation analysis, thereby offering new ways of analyzing and interpreting sets of potential risk factors in relation to sets of clinical outcomes.

Aged↗

Improving the spatial specificity of canonical correlation analysis in fMRI.

The contrast-to-noise ratio (CNR) is often very low in fMRI data, and standard univariate methods suffer from a loss of sensitivity in the context of noise. The increased power of a multivariate statistical analysis method known as canonical correlation analysis (CCA) in fMRI studies with low CNR was established previously. However, CCA in its conventional form has weak spatial specificity. In this work we propose a new assignment scheme to rectify this problem. It is shown that the new method has improved spatial specificity as well as sensitivity compared to conventional CCA for detecting activation patterns in fMRI.

Adult↗

Socio-economic and urbanization profiles of internal migration in Egypt: a canonical correlation analysis.

"Data on in- and out-migration in Egypt by governorate and on variables believed to interact with migration are examined. The purpose is to identify the dimensions through which the basic demographic phenomenon can be rationally structured. This objective is approached through a general multivariate analysis technique known as canonical correlation analysis....A number of variables believed to act as determinants of migration are used and arbitrarily classified into four groups to represent four distinct migration profiles, namely, sociodemographic, economic, urbanization, and health profiles.... The analysis shows that the economic profile of migration is the most pronounced one of the four profiles examined."

Africa↗

Comparison of two quality-of-life instruments for cancer patients: the functional assessment of cancer therapy-general and the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire-C30.

PURPOSE: To compare two quality-of-life (QOL) questionnaires for cancer patients, the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire-C30 (EORTC QLQ-C30) and the Functional Assessment of Cancer Therapy-General (Fact-G), on the basis of empirical data. PATIENTS AND METHODS: Two hundred forty-four patients with a diagnosis of breast cancer or Hodgkin's disease completed both the EORTC QLQ-C30 and the FACT-G (German language version) during the same session. Questionnaire data were analyzed on a subscale basis using correlation analysis, canonical correlation, and multiple linear regression. RESULTS: Correlations between corresponding subscales of the FACT-G and the EORTC QLQ-C30 ranged from r =.14 for the social domain (very poor agreement) to r =.66 for the physical domain (good agreement), with r values for the other domains lying between these extremes. Canonical correlation analysis for the two sets of subscales revealed that overall agreement between the two instruments was only moderate (first canonical correlation coefficient r =.85, but overall redundancy less than 40%). Of the five FACT-G subscales, only one, physical well-being, was well represented by the EORTC QLQ-C30 subscales (multiple linear regression, R(2) =.67). Only three of eight EORTC QLQ-C30 subscales (physical functioning, global QOL, general symptoms) were represented fairly well by FACT-G subscales (R(2) =.43 to.60). The lowest R(2) values (<.15; ie, virtually no representation by the other instrument) were found for the FACT-G social well-being and relation with doctors and EORTC QLQ-C30 cognitive functioning subscales. CONCLUSION: For the sample investigated, the EORTC QLQ-C30 and the FACT-G were found to measure markedly different aspects of QOL, despite considerable overlap. Replicability provided, this implies that neither of the two QOL instruments can be replaced by the other and that a direct comparison of results obtained with the two instruments is not possible.

Adult↗

Kernel and nonlinear canonical correlation analysis.

We review a neural implementation of the statistical technique of Canonical Correlation Analysis (CCA) and extend it to nonlinear CCA. We then derive the method of kernel-based CCA and compare these two methods on real and artificial data sets before using both on the Blind Separation of Sources.

Algorithms↗

Facial expression recognition using kernel canonical correlation analysis (KCCA).

In this correspondence, we address the facial expression recognition problem using kernel canonical correlation analysis (KCCA). Following the method proposed by Lyons et al. and Zhang et al., we manually locate 34 landmark points from each facial image and then convert these geometric points into a labeled graph (LG) vector using the Gabor wavelet transformation method to represent the facial features. On the other hand, for each training facial image, the semantic ratings describing the basic expressions are combined into a six-dimensional semantic expression vector. Learning the correlation between the LG vector and the semantic expression vector is performed by KCCA. According to this correlation, we estimate the associated semantic expression vector of a given test image and then perform the expression classification according to this estimated semantic expression vector. Moreover, we also propose an improved KCCA algorithm to tackle the singularity problem of the Gram matrix. The experimental results on the Japanese female facial expression database and the Ekman's "Pictures of Facial Affect" database illustrate the effectiveness of the proposed method.

Algorithms↗

Canonical correlation analysis: an overview with application to learning methods.

We present a general method using kernel canonical correlation analysis to learn a semantic representation to web images and their associated text. The semantic space provides a common representation and enables a comparison between the text and images. In the experiments, we look at two approaches of retrieving images based on only their content from a text query. We compare orthogonalization approaches against a standard cross-representation retrieval technique known as the generalized vector space model.

Journal Article↗