Method and system for scalable multi-task learning with convex clustering

Abstract

A method for scalable multi-task learning with convex clustering includes: extracting features from a dataset of a plurality of tasks; generating a graph from the extracted features, nodes of the graph representing linear learning models, each of the linear learning models being for one of the tasks; constraining the graph using convex clustering to generate a convex cluster constrained graph; and obtaining a global solution by minimizing a graph variable loss function, the minimizing the graph variable loss function comprising: introducing auxiliary variables for each connection between nodes in the convex cluster constrained graph; iteratively performing the following operations until convergence: updating the linear learning models by solving a sparse linear system; and updating the auxiliary variables by solving an equation having the auxiliary variables each be proportional to a vector norm for their respective nodes.