Tensorial Change Analysis Using Probabilistic Tensor Regression

Change correlations in structure-activity studies using multiple regression analysis.

Probabilistic Solar Forecasting Using Quantile Regression Models

In this work, we assess the performance of three probabilistic models for intra-day solar forecasting. More precisely, a linear quantile regression method is used to build three models for generating 1 h–6 h-ahead probabilistic forecasts. Our approach is applied to forecasting solar irradiance at a site experiencing highly variable sky conditions using the historical ground observations of sola...

Probabilistic Regression Using Basis Function Models

Our goal is to accurately estimate the error in any prediction of a regression model. We propose a probabilistic regression framework for basis function regression models, which includes widely used kernel methods such as support vector machines and nonlinear ridge regression. The framework outputs a point specific estimate of the probability that the true regression surface lies between two us...

Probabilistic Tensor Factorization for Tensor Completion

Multi-way tensor datasets emerge naturally in a variety of domains, such as recommendation systems, bioinformatics, and retail data analysis. The data in these domains usually contains a large number of missing entries. Therefore, many applications in those domains aim at missing value prediction, which boils down to a tensor completion problem. While tensor factorization algorithms can be a po...

Probabilistic Latent Tensor Factorization

We develop a probabilistic framework for multiway analysis of high dimensional datasets. By exploiting a link between graphical models and tensor factorization models we can realize any arbitrary tensor factorization structure, and many popular models such as CP or TUCKER models with Euclidean error and their non-negative variants with KL error appear as special cases. Due to the duality betwee...