US 11,657,322 B2
Method and system for scalable multi-task learning with convex clustering
Xiao He, Heidelberg (DE); Francesco Alesiani, Heidelberg (DE); and Ammar Shaker, Heidelberg (DE)
Assigned to NEC CORPORATION, Tokyo (JP)
Filed by NEC Laboratories Europe GmbH, Heidelberg (DE)
Filed on May 17, 2019, as Appl. No. 16/414,812.
Claims priority of provisional application 62/724,704, filed on Aug. 30, 2018.
Prior Publication US 2020/0074341 A1, Mar. 5, 2020
Int. Cl. G06N 20/00 (2019.01); G06F 17/11 (2006.01)
CPC G06N 20/00 (2019.01) [G06F 17/11 (2013.01)] 15 Claims
OG exemplary drawing
1. A method for scalable multi-task 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.