The multivariate reduced rank regression model plays an important role in econometrics. Examples include co-integration analysis and models with a factor structure. Geweke (1996) provided the foundations for a Bayesian analysis of this model. Unfortunately several of the full conditional posterior distributions, which forms the basis for constructing a Gibbs sampler for the poster distribution,...

In the last few years, Facial Expression Synthesis (FES) has been a flourishing area of research driven by applications in character animation, computer games, and human computer interaction. This paper proposes a photorealistic FES method based on Bilinear Kernel Reduced Rank Regression (BKRRR). BKRRR learns a high-dimensional mapping between the appearance of a neutral face and a variety of e...

While there is strong motivation for using Gaussian Processes (GPs) due to their excellent performance in regression and classification problems, their computational complexity makes them impractical when the size of the training set exceeds a few thousand cases. This has motivated the recent proliferation of a number of cost-effective approximations to GPs, both for classification and for regr...

Reduced rank regression extracts an essential information from examples of input-output pairs. It is understood as a three-layer neural network with linear hidden units. However, reduced rank approximation is a non-regular statistical model which has a degenerate Fisher information matrix. Its generalization error had been left unknown even in statistics. In this paper, we give the exact asympt...

Abstract Logistic reduced rank regression is a useful data analysis tool when we have multiple binary response variables and set of predictors. In this paper, describe logistic present new majorization minimization algorithm for the estimation model parameters. Furthermore, discuss Type I D triplots visualizing results model, compare them, then develop hybrid triplot using elements both types. ...