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...

Bulking and foaming are two notorious problems in activated sludge wastewater treatment plants (WWTPs), which are mainly associated with the excessive growth of bulking and foaming bacteria (BFB). However, studies on affecting factors of BFB in full-scale WWTPs are still limited. In this study, data sets of high-throughput sequencing (HTS) of 16S V3-V4 amplicons of 58 monthly activated sludge s...

In this paper we describe a method for functional connectivity analysis of fMRI data between given brain regions-of-interest (ROIs). The method relies on nonnegativity constrained- and spatially regularized multiset canonical correlation analysis (CCA), and assigns weights to the fMRI signals of the ROIs so that their representative signals become simultaneously maximally correlated. The differ...

In this paper, a new statistical process analysis and quality prediction method is proposed for multiphase batch processes. A two-level phase division algorithm is designed to capture and trace quality-related inner-phase evolution which in general goes through three statuses sequentially, i.e., transition, steady-phase and transition. Partial least squares (PLS), canonical correlation analysis...

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.

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...