Latent space harmonization for predictive modeling

Abstract

In embodiments of latent space harmonization (LSH) for predictive modeling, different training data sets are obtained from different measurement methods, where input data among the training data sets is quantifiable in a common space but a mapping between output data among the training data sets is unknown. A LSH module receives the training data sets and maps a common supervised target variable of the output data to a shared latent space where the output data can be jointly yielded. Mappings from the shared latent space back to the output training data of each training data set are determined and used to generate a trained predictive model. The trained predictive model is useable to predict output data from new input data with improved predictive power from the training data obtained using various, otherwise incongruent, measurement techniques.