Canonical Correlation Analysis

Feminist writers critical of the codependency model have argued that codependency refers to an overly strong conformity to the traditional feminine role. Canonical correlation analysis using data from 192 first-year university students found a moderate association between codependency and gender-role identification. The specific nature of this association varied with different aspects of codependency. External focus (approval seeking) was associated with

There is no empirical evidence in Jordan that addresses the overlap and coexistence of overexcitability (OE) and attention deficit hyperactivity disorder (ADHD) in gifted students. This study aims to investigate (a) the relationship between characteristics of OE forms and ADHD subtypes and (b) the gender differences in OE profiles among gifted students in Jordan. The participants included 265 gifted adolescents from the Jubilee Institute. They were administered the Jordanian version of the Overexcitability Questionnaire-Two (OEQII) and the Conners ADHD/DSM-V Scales-Adolescent scale. The Canonical Correlation Analysis (CCA) revealed a noteworthy collective relationship between OE and ADHD constructs, and another noteworthy relationship in relation to the gender of gifted adolescents. The findings also revealed small, significant positive correlations between (a) Psychomotor OE and hyperactive-impulsive ADHD, (b) Imaginational OE and ADHD subtypes, and (c) a small significant negative correlation between Intellectual OE and inattentive ADHD scores. Additionally, there was a significant gender difference in the Psychomotor OE in favor of boys and significant differences in the Emotional, Sensual, and Imaginational OEs in favor of girls. No significant gender differences were found in the Intellectual OEs of the participants.

Bills of quantities (BoQs) doubtlessly remain the most preferred cost management instrument in construction
procurements globally. Inadvertently, however, the potential of BoQ errors to cause construction disputes is
largely ignored in literature. This study determined the sources of BoQ errors and the relationship between BoQ
errors and construction disputes. Data were gathered by opinion survey of construction professionals and
contractors in Edo State, Nigeria. The multivariate techniques of factor analysis, canonical correlation and
logistic regression were used in the analyses. It was found that the sources of BoQ errors can be summarised as
designer's competence, BoQ compiler's competence, designer's mistakes and BoQ compiler's mistakes. Apart
from designer's mistakes, all the factors were found to be significantly related to the types of errors actually found
in BoQs. The BoQ errors that lead to disputes were identified as inaccurate quantities, inadequate unit rates, and
measuring mechanical and electrical works as provisional sums. To address professional incompetence and
mistakes in the preparation of BoQs, government should insist that only drawings and BoQs that are sealed by the
appropriate professionals are acceptable for the award of public contracts, and construction professionals should
ensure that they carefully scrutinize works done by their junior staff before such works are sent out for use in
construction procurement. The paper offers insights into the relationship between errors in BoQs and
construction disputes.

The objective of this work is to automatically recognize faces from video sequences in a realistic, unconstrained setup in which illumination conditions are extreme and greatly changing, viewpoint and user motion pattern have a wide variability, and video input is of low quality. At the centre of focus are face appearance manifolds: this thesis presents a significant advance of their understanding and application in the sphere of face recognition. The two main contributions are the Generic Shape-Illumination Manifold recognition algorithm and the Anisotropic Manifold Space clustering.

The Generic Shape-Illumination Manifold algorithm shows how video sequences of unconstrained head motion can be reliably compared in the presence of greatly changing imaging conditions. Our approach consists of combining a priori domain-specific knowledge in the form of a photometric model of image formation, with a statistical model of generic face appearance variation. One of the key ideas is the reillumination algorithm which takes two sequences of faces and produces a third, synthetic one, that contains the same poses as the first in the illumination of the second.

The Anisotropic Manifold Space clustering algorithm is proposed to automatically determine the cast of a feature-length film, without any dataset-specific training information. The method is based on modelling coherence of dissimilarities between appearance manifolds: it is shown how inter- and intra-personal similarities can be exploited by mapping each manifold into a single point in the manifold space. This concept allows for a useful interpretation of classical clustering approaches, which highlights their limitations. A superior method is proposed that uses a mixture-based generative model to hierarchically grow class boundaries corresponding to different individuals.

The Generic Shape-Illumination Manifold is evaluated on a large data corpus acquired in real-world conditions and its performance is shown to greatly exceed that of state-of-the-art methods in the literature and the best performing commercial software. Empirical evaluation of the Anisotropic Manifold Space clustering on a popular situation comedy is also described with excellent preliminary results.

The Vimalakīrti Nirdeśa Sūtra, an influential Mahāyāna scripture, is famous for its used stories for the purpose of preaching, in particular, “A Goddess Scattering Flowers”, an internationally renowned scene which expounds on major Mahāyāna teachings. By analysing its dramatic representations, the current study elaborates on the doctrine of indiscrimination, grounded in the law of dependent origination, voidness, impermanence, non-self, non-duality, and dharma. It also presents the effectiveness of using humour, metaphors, similes, conflicts, contrasts, contradictions, and paradoxes to powerfully delineate rich connotations through character performance, stage property, stage setting, dialogue, and action. With the aid of these theatrical components and techniques, this demonstrates how such a brief scene within a fable is capable of explaining a cluster of Buddhist philosophies pertaining to self-transcendence, self-management of mind, and freedom from dichotomy, formalism, and self-boundary; in conclusion, enlightenment. This theatrical presentation highlight may allow scholars to further explore alternatives for philosophical discussion.

Canonical correspondence analysis (CCA) is a multivariate method to elucidate the relationships between biological assemblages of species and their environment. The method is designed to extract synthetic environmental gradients from ecological data-sets. The gradients are the basis for succinctly describing and visualizing the differential habitat preferences (niches) of taxavia an ordination diagram. Linear multivariate methods for relating two set of variables, such as two-block Partial Least Squares (PLS2), canonical correlation analysis and redundancy analysis, are less suited for this purpose because habitat preferences are often unimodal functions of habitat variables. After pointing out the key assumptions underlying CCA, the paper focuses on the interpretation of CCA ordination diagrams. Subsequently, some advanced uses, such as ranking environmental variables in importance and the statistical testing of effects are illustrated on a typical macroinvertebrate data-set. The paper closes with comparisons with correspondence analysis, discriminant analysis, PLS2 and co-inertia analysis. In an appendix a new method, named CCA-PLS, is proposed that combines the strong features of CCA and PLS2.

Penyakit pneumonia merupakan salah satu penyakit yang paling menyita perhatian praktisi kesehatan. Pneumonia menjadi penyakit utama penyebab kematian pada balita. Hal tesebut disebabkan oleh faktor intenal dan eksternal penderita. Tingkat pneumonia pada balita masih dikatakan tinggi khususnya di Indonesia. Data yang digunakan adalah data sekunder yang diperoleh dari buku Data dan Informasi Profil Kesehatan Indonesia 2016. Dalam penelitian ini, peneliti menggunakan teknik korelasi kanonikal untuk mengetahui faktor dominan diantara indikator lingkungan hidup sehat 2016 di Indonesia terhadap tingkat pneumonia pada balita (pneumonia dan pneumonia berat) 2016. Hasil analisis korelasi kanonikal menunjukkan bahwa dari 4 variabel independen yang ada 1 variabel yang memiliki hubungan yang sangat tinggi yaitu variabel x1. Variabel x1 merepresentasikan presentase jumlah sumber air minum bersih yang ada pada masing-masing daerah. Berdasarkan pada output yang diperoleh tampak bahwa dari 2 variabel dependen, variabel y2 memiliki hubungan yang paling kuat. Nilai korelasi untuk variabel y2 adalah 0,5728528. Hal tersebut berarti variabel dependen y2 yaitu presentase jumlah balita yang terindikasi pneumonia berat lebih berkorelasi dengan variabel independen secara bersama-sama daripada variabel dependen y1 atau presentase jumlah balita penderita pneumonia (biasa).

Regularized generalized canonical correlation analysis (RGCCA) is a generalization of regularized canonical correlation analysis to three or more sets of variables. It constitutes a general framework for many multi-block data analysis methods. It combines the power of multi-block data analysis methods (maximization of well identified criteria) and the flexibility of PLS path modeling (the researcher decides which blocks are connected and which are not). Searching for a fixed point of the stationary equations related to RGCCA, a new monotonically convergent algorithm, very similar to the PLS algorithm proposed by Herman Wold, is obtained. Finally, a practical example is discussed.