Canonical correlation analysis of marine macrobenthos survey data

In recent years, the number of people with Alzheimer’s disease (AD) has grown worldwide. Since no cure has yet been found for the disease, detection and initiation of treatment is the best way to prevent the disease from progressing behavioral and physical symptoms in the patient. There are several methods to diagnose AD, and neuroimaging, as a noninvasive approach, reveals changes in the brain due to the disease. Diagnosis accuracy can be improved by fusion of various neuroimaging modalities. Canonical correlation analysis (CCA) and its extensions have been widely used for fusing multi-modal datasets, where healthy controls (HCs) are differentiated from patients by applying classification methods to canonical variables (CVs) resulting from CCA or its extensions. The goal of our study is to find an optimal method, from the perspective of accuracy and processing complexity, to diagnose patients with AD. This goal is achieved by fusing anatomical magnetic resonance imaging (MRI) and functional MRI (fMRI) data using CCA-based methods. Experimental results illustrate that HCs and AD patients can be classified using CVs obtained from structured and sparse CCA with greater than 90% accuracy.

Objective:In this study; the relationships of several traits effects of Turkish Hair Goats were investigated by canonic correlation analysis. Therefore determining more effective factors on weaning weight of Hair goats are aimed.Material and Methods: The data were utilized from 1020 male and 1011 female Hair goat kids which were born in the period 2013 to 2016. The herd, year of birth, birth type and sex are analyzed as the first set of variables (X); weight at birth, weight at thirty day of age and weight at weaning age effects are analyzed as the second set of variables (Y). By performing canonical correlation analysis, canonical correlations between the first, second and third pair of canonical variates were estimated and three canonical correlation were found significant (P < 0.01).Results:The biggest correlations between year of birth, sex, birth type variables and v1, v2 and v3 canonical variates were found -0.77%, 0.87% and -0.82%, respectively. The biggest correlations between weight at birth, weight at thirty day of age and weight at weaning age and w1, w2 and w3 canonical variates were found 0.84%, -0.97% and -0.78%, respectively. As a result it has been determined that sex and birth type play an important role in the formation of the goat kid’s body weights.Conclusion:Hair goats reared in West Anatolia are slaughtered mostly at the end of the weaning. Because of this, assessment the factors effect on weaning weight are important. Canonical correlation analysis therefore is used to analyze all factors at the same time to define relationships between each other of weaning traits for hair goats.

The objective of this study was to investigate the multivariate relationship among body protein, fat, and macrominerals in Saanen goats of different sexes (castrated males, females, and intact males) using canonical correlation analysis. Individual records of 274 Saanen goats combined from 10 studies was used. Two sets of body constituents were established: the first one contained variables related to body protein or fat (canonical variate U) and the second contained variables related to body calcium, phosphorus, magnesium, sodium, or potassium (canonical variate V). Two canonical pairs were identified for each sex. However, irrespective of sex, first canonical pairs accounted for more than 87% of variance of the dependent variables, these only being used for the analysis. For canonical variate U1, canonical weights for protein were greater than that for fat (in castrated males, protein = 0.62 and fat = 0.41; in females, protein = 0.96 and fat = 0.039; and in intact males, protein = 0.81 and fat = 0.20). For canonical variate V1, in males, the highest canonical weights were for potassium and phosphorus, whereas the lowest were for calcium (in castrated males, potassium = 0.485 > phosphorus = 0.259 > magnesium = 0.206 > sodium = 0.129 > calcium = 0.081, and in intact males, potassium = 0.499 > phosphorus = 0.459 > sodium = 0.105 > magnesium = 0.024 > calcium = 0.001). On the other hand, in females, the highest canonical weights were for potassium and calcium, whereas the lowest was for magnesium (potassium = 0.539 > calcium = 0.201 > phosphorus = 0.178 > sodium = 0.088 > magnesium = 0.081). The current results may help to understand the role of sex on strength and nature of the association among body protein, fat, and macrominerals in growing Saanen goats.

Electroencephalogram (EEG) signal is of- ten contaminated by electronic noise as well as move- ment artifacts. This paper presented an algorithm based on Canonical correlation analysis (CCA) to estimate multi- channel EEG data. Different from previous studies, in which CCA was mainly used to detect the invariant fea- tures specific to each brain state, in this paper, the canon- ical variates computed by CCA were used to reconstruct the multi-channel EEG data. Firstly, two data sets, EEG signals and the reference signals based on prior knowledge were constructed. Next, canonical variates were computed by projecting the two data sets onto basis vectors. Finally, a least squares solution was used to estimate the multi- channel EEG data. The experiment results suggested that the algorithm is capable of reconstructing the actual spe- cific components with high quality. We also hint future possible application of the algorithm in the estimation of functional connectivity patterns at the end of the paper.

This paper gives results from joint analyses of dual polarimety synthetic aperture radar data from the Sentinel-1 mission and optical data from the Sentinel-2 mission. The analyses are carried out by means of traditional canonical correlation analysis (CCA) and canonical information analysis (CIA). Where CCA is based on maximising correlation between linear combinations of the two data sets, CIA maximises mutual information between the two. CIA is a conceptually more pleasing method for the analysis of data with very different modalities such as radar and optical data. Although a little inconclusive as far as the change detection aspect is concerned, results show that CIA analysis gives conspicuously less noisy appearing images of canonical variates (CVs) than CCA. Also, the 2D histogram of the mutual information based leading CVs clearly reveals much more structure than the correlation based one. This gives promise for potentially better change detection results with CIA than can be obtained by means of CCA.

Investigating γ- and X-ray properties of GRBs using multivariate statistics

We examined the affects of selected water quality variables on the presence of subadult sharks in six of nine Georgia estuaries. During 231 longline sets, we captured 415 individuals representing nine species. Atlantic sharpnose shark (Rhizoprionodon terranovae), bonnethead (Sphyrna tiburo), blacktip shark (Carcharhinus limbatus) and sandbar shark (C. plumbeus) comprised 96.1% of the catch. Canonical correlation analysis (CCA) was used to assess environmental influences on the assemblage of the four common species. Results of the CCA indicated Bonnethead Shark and Sandbar Shark were correlated with each other and with a subset of environmental variables. When the species occurred singly, depth was the defining environmental variable; whereas, when the two co-occurred, dissolved oxygen and salinity were the defining variables. Discriminant analyses (DA) were used to assess environmental influences on individual species. Results of the discriminant analyses supported the general CCA findings that the presence of bonnethead and sandbar shark were the only two species that correlated with environmental variables. In addition to depth and dissolved oxygen, turbidity influenced the presence of sandbar shark. The presence of bonnethead shark was influenced primarily by salinity and turbidity. Significant relationships existed for both the CCA and DA analyses; however, environmental variables accounted for <16% of the total variation in each. Compared to the environmental variables we measured, macrohabitat features (e.g., substrate type), prey availability, and susceptibility to predation may have stronger influences on the presence and distribution of subadult shark species among sites.

The application of three multivariate analysis techniques (canonical discriminant analysis (CDA), principal component analysis (PCA), and canonical correlation analysis (CCA)) for evaluation of pasture botanical composition data is illustrated and discussed. CDA and PCA were used to describe differences in pasture botanical composition for different microsites within a pasture near Palmerston North, New Zealand. CCA could not be validly applied to this data set because a sampling strategy inappropriate for CCA had been used to collect the data. However, CCA is conceptually ideal for determining association between two groups of variables and CCA was used for a second data set from the Hawkes Bay region to establish association between differences in pasture botanical composition and differences in environmental variables. CCA identified a transition from white clover (Trifolium repens L.) to subterranean clover {Trifolium subterraneum L.) presence associated with decreasing rainfall, and a similar transition from ryegrass presence to browntop presence associated with altitude. There are practical difficulties in obtaining a suitable data set for canonical correlation, but with attention to sampling strategy a more precise definition of effect of environmental factors on pasture botanical composition would be expected.

Canonical correlation analysis (CCA) is a powerful technique for assessing the relationship between two sets of variables. However, classical CCA does not distinguish between treatment and outcome variables, and it fails to account for potential confounding effects that may bias the estimated results. This study proposes a novel method called canonical causal analysis (Causal‐CCA) that extends CCA to estimate the canonical causal effects between multivariate continuous treatments and outcomes while adjusting for confounding variables. Causal effects are estimated within a framework where treatments and outcomes are prespecified. The proposed approach uses Gram–Schmidt orthogonalization and entropy balancing weights for multivariate treatments (EBMT) to extract unconfounded canonical causal variates and compute the canonical causal effects. Extensive simulations across various scenarios demonstrate that Causal‐CCA consistently outperforms classical CCA, weighted CCA with multivariate generalized propensity score weighting (MVGPS) and CCA with EBMT in terms of bias and mean squared error. The proposed method is applied to a real‐world dataset to study the causal relationship between psychological factors (self‐concept and motivation) and academic achievement (math and science scores), showing a significant positive causal effect of the psychological variables on academic performance.

One hundred male obstructive sleep apnea (OSA) patients were classified into 2 groups on the basis of body mass index (BMI): 43 nonobese (BMI < 30 kg/m2) and 57 obese (BMI > or = 30 kg/m2) patients. A comprehensive cephalometric analysis with a multivariate statistical method was performed in order to define the different principal components (PCs) of cervico-craniofacial skeletal and upper airway soft tissue morphology in each group and how they contributed to selected elements of the patient demographic data, ie, apnea-hypopnea index (AHI), nocturnal oxyhemoglobin saturation, and BMI. Thirty cephalometric variables of cervico-craniofacial skeletal morphology were reduced to 8 PCs describing 84.4% and 85.4% of the total variance in obese and nonobese OSA patients, respectively. Sixteen cephalometric variables of hyoid bone position and head posture were reduced to 4 PCs describing 84.4% and 85.9% of the total variance in obese and nonobese OSA patients, respectively. Twenty cephalometric variables of upper airway soft tissue morphology were reduced to 7 PCs describing 89.5% and 84.6% of the total variance in obese and nonobese OSA patients, respectively. For further analysis of PCs, a stepwise multiple regression analysis was chosen. Two dependent variables of interest are the minimal distance of the posterior pharyngeal airway space (PASmin) and AHI. PASmin accounted for 95.3% (obese OSA group) and 74.3% (nonobese OSA group) with 7 PCs and AHI for 46% with 3 PCs in both groups. Three canonical variables and their correspondents with different loadings were established differently for both OSA groups. A canonical correlation successfully clarified the complexity of simultaneous relationship of the relevant variables. These analyses are proved useful to demonstrate the relationship of cervico-craniofacial skeletal and upper airway soft tissue morphology and selected demographic data. This lays down a basis for understanding the complicated pathogenic components of obese and nonobese OSA patients.

Morphological traits and spatial heterogeneity of aquatic plants along sediment and depth gradients, Lake Geneva, Switzerland

In the face of multicollinearity, researchers face challenges interpreting canonical correlation analysis (CCA) results. Although standardized function and structure coefficients provide insight into the canonical variates produced, they fall short when researchers want to fully report canonical effects. This article revisits the interpretation of CCA results, providing a tutorial and demonstrating canonical commonalty analysis. Commonality analysis fully explains the canonical effects produced by using the variables in a given canonical set to partition the variance of canonical variates produced from the other canonical set. Conducting canonical commonality analysis without the aid of software is laborious and may be untenable, depending on the number of noteworthy canonical functions and variables in either canonical set. Commonality analysis software is identified for the canonical correlation case and we demonstrate its use in facilitating model interpretation. Data from Holzinger and Swineford (1939) are employed to test a hypothetical theory that problem-solving skills are predicted by fundamental math ability.

Intensive land use is requiring more detailed information about patterns and magnitudes of soil variability than can be acquired through traditional soil survey techniques. Discriminant analysis is a mathematical method of numerical classification which could be used to identify discrete populations of soils in their natural settings. The hypothesis of this study was that discriminant analysis could be used to group soils on landtypes on the Mid-Cumberland Plateau. A large data set (132 observations of 29 soil variables) was collected from three landtypes at two Cumberland Plateau locations. Discriminant analysis was used to classify the soil observations into landtypes. Canonical correlation was used to identify soil properties most responsible for separating soils into groups related to landtypes. Not all of the collected soil properties were important discriminators, so variables with low canonical loading scores were eliminated. A total of 13 soil variables representing three genetic soil horizons was required to correctly classify all 132 observations into correct landtypes. Canonical correlations were 0.979 and 0.970 with 29 variables and 0.968 and 0.941 with 13 variables on canonical variates one and two, respectively. Soil variables from Bt horizons alone did not classify all observations into correct landtypes. Discriminant analysis, in conjunction with canonical correlation, shows promise for identifying key variables for numerically classifying soils into related populations.

A rapid method based on front-face fluorescence spectroscopy for the monitoring of the texture of meat emulsions and frankfurters

Canonical correlation analysis (CCA) is a kind of classical multivariate analysis method. Less canonical correlation variables are used to describe the relationship between two variables completely but easily. To get high face recognition rate under low-resolution degradation over a long distance solidly, in this work, CCA is used to extract the correlation between high-resolution face images and low-resolution ones and to find the transform pair between them. Therefore, face images of the same individual with variable resolutions can be matched accurately. This is the first method that uses CCA to do low-resolution degradation face recognition over long distances. We conduct the experiments on the Extended Yale B and ORL database, and the experimental results validate the efficacy of the proposed method.