In this paper we purpose a blockwise descent algorithm for group-penalized multiresponse regression. Using a quasi-newton framework we extend this to group-penalized multinomial regression. We give the first publicly available implementation for these in R, and compare the speed of this algorithm to a similar algorithm for standard `1-penalized multinomial regression on simulated data — we show...

This paper presents a new methodology for missing value imputation in a database. The methodology combines the outputs of several Self-Organizing Maps in order to obtain an accurate filling for the missing values. The maps are combined using MultiResponse Sparse Regression and the Hannan-Quinn Information Criterion. The new combination methodology removes the need for any lengthy cross-validati...

This paper presents a new methodology for missing value imputation in a database. The methodology combines the outputs of several Self-Organizing Maps in order to obtain an accurate filling for the missing values. The maps are combined using MultiResponse Sparse Regression and the Hannan-Quinn Information Criterion. The new combination methodology removes the need for any lengthy cross-validati...

The problem of multiclass classification is considered and resolved through the multiresponse linear regression approach. Scores are used to encode the class labels into multivariate responses. The regression of scores on input attributes is used to extract a lowdimensional linear discriminant subspace. The classification training and prediction are carried out in this low-dimensional subspace....

Under the conditions used to prepare the traditional sugar syrups and caramels, commercial sucrose is heated with small amounts of water in order to attain supersaturated (at room temperature) solutions. Undesuch conditions, a lag phase in sucrose thermal degradation was experimentally observed. This lag phase was also observed in the formation of reaction products, and can be interpreted has a...