The canonical correlation analysis has been successfully used in many areas aiming to extract important information from a pair of data sets. Thus, the objective this work was determine sample size (number plants) required estimate correlations corn characteristics. Six characteristics were measured 361, 373, and 416 plants, respectively, single, three-way double cross hybrids 2008/2009 crop ye...

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 ...

In this paper we develop a canonical state space representation for rational stochastic processes containing unit roots with integer integration orders at arbitrary points on the unit circle. It is shown that the state space framework, which is – in a certain sense made precise in the paper – equivalent to the ARMA framework, is very suitable for the analysis of unit roots and cointegration iss...