CPC G06N 20/00 (2019.01) [G06F 17/11 (2013.01)]  15 Claims 
1. A method for scalable multitask learning with convex clustering, the method comprising:
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 a vector norm for their respective nodes.
