Generalized Canonical Correlation Analysis (GCCA) is an important tool that finds numerous applications in data mining, machine learning, and artificial intelligence. It aims at finding `common' random variables are strongly correlated across multiple feature representations (views) of the same set entities. CCA to a lesser extent GCCA have been studied from statistical algorithmic points view,...

We present a novel approach for multiview canonical correlation analysis based on variational graph neural network model. propose nonlinear model which takes into account the available graph-based geometric constraints while being scalable to large-scale datasets with multiple views. This combines probabilistic interpretation of CCA an autoencoder architecture convolutional layers. Experiments ...

Classical canonical correlation analysis (CCA) requires matrices to be low dimensional, i.e. the number of features cannot exceed sample size. Recent developments in CCA have mainly focused on high-dimensional setting, where both under greatly exceeds These approaches impose penalties optimization problems that are needed solve iteratively, and estimate multiple vectors sequentially. In this wo...

During the past decade, the World Economic Forum has published its annual reports in which the Global Competitiveness Index is included. This paper aims to investigate the key factors for achieving an innovation-driven economy. In this paper, we used partial canonical correlation analysis (PCCA) to examine the relationships between key pillars in “efficiency enhancers” and “business sophisticat...

By elucidating a parallel between canonical correlation analysis (CCA) and least squares regression (LSR), we show how regularization of CCA can be performed and interpreted in the same spirit as the regularization applied in ridge regression (RR). Furthermore, the results presented may have an impact on the practical use of regularized CCA (RCCA). More specifically, a relevant cross validation...

Kernel Canonical correlation analysis (KCCA) is a fundamental method with broad applicability in statistics and machine learning. Although there exist closedform solution to the KCCA objective by solving an N × N eigenvalue system where N is the training set size, the computational requirements of this approach in both memory and time prohibit its usage in the large scale. Various approximation...

Knowledge of the relationship between model parameters and forecast quantities is useful because it can aid in setting the values of the former for the purpose of having a desired effect on the latter. Here it is proposed that a well-establishedmultivariate statistical method known as canonical correlation analysis can be formulated to gauge the strength of that relationship. Themethod is appli...

1. Canonical correlation analysis (CCA) is a technique to extract common features from a pair of multivariate data. In complex situations, however, it does not extract useful features because of its linearity. On the other hand, kernel method used in support vector machine (Vapnik, 1998) is an efficient approach to improve such a linear method. In this study, we investigate the effectiveness of...