A multivariate technique named Canonical Concordance Correlation Analysis (CCCA) is introduced. In contrast to the classical (CCA) which based on maximization of Pearson’s correlation coefficient between linear combinations two sets variables, CCCA maximizes Lin’s concordance accounts not just for maximum but also closeness aggregates’ mean values and their variances. While CCA employs centered...

the main objective of this study was using of canonical correlation analysis (cca) to find relations between 15 different variables of seed emergence and seedling growth related characteristics of seven important crop that were classified in three groups. the studied groups of variables were included physical characteristics (seed weight, seed length, seed density, water absorption and water co...

present study seeks to investigate the existing canonical correlation among the effective factors including x variables that consist of environmental factors, inter-organizational factors and the creation factors of the perceptual value; on the components of competitiveness, as well as y variables consisting of enterprise performance against the market, the customers and the internal performanc...

in order to have a successful breeding program, it is important to determine the relationship among the traits. this study was conducted at research farm of isfahan university of technology to evaluate relationships among some of the agronomic and physiological traits and grain yield of ten bread wheat cultivars in optimum and stress moisture (irrigation after 70±3 and 130±3 mm evaporation from...

We tightly analyze the sample complexity of CCA, provide a learning algorithm that achieves optimal statistical performance in time linear in the required number of samples (up to log factors), as well as a streaming algorithm with similar guarantees.

Canonical correlation analysis (CCA) is a classical representation learning technique for finding correlated variables in multi-view data. Several nonlinear extensions of the original linear CCA have been proposed, including kernel and deep neural network methods. These approaches seek maximally correlated projections among families of functions, which the user specifies (by choosing a kernel o...

Given a bivariate distribution, the set of canonical correlations and functions is in general finite or countable. By using an inner product between two functions via an extension of the covariance, we find all the canonical correlations and functions for the so-called Cuadras-Augé copula and prove the continuous dimensionality of this distribution.