Multi-task learning using bayesian model with enforced sparsity and leveraging of task correlations

摘要

Multi-task regression or classification includes optimizing parameters of a Bayesian model representing relationships between D features and P tasks, where D≧1 and P≧1, respective to training data comprising sets of values for the D features annotated with values for the P tasks. The Bayesian model includes a matrix-variate prior having features and tasks dimensions of dimensionality D and P respectively. The matrix-variate prior is partitioned into a plurality of blocks, and the optimizing of parameters of the Bayesian model includes inferring prior distributions for the blocks of the matrix-variate prior that induce sparseness of the plurality of blocks. Values of the P tasks are predicted for a set of input values for the D features using the optimized Bayesian model. The optimizing also includes decomposing the matrix-variate prior into a product of matrices including a matrix of reduced rank in the tasks dimension that encodes correlations between tasks.

主权项

1. An apparatus comprising:
an electronic data processing device configured to perform a method comprising:
constructing a Bayesian model representing relationships between a plurality of features and a plurality of tasks wherein the Bayesian model includes a matrix-variate Gaussian scaled mixture prior having a features dimension and a tasks dimension and wherein the matrix-variate Gaussian scaled mixture prior is partitioned into a plurality of blocks;generating an optimized Bayesian model by optimizing parameters of the Bayesian model respective to training data comprising sets of feature values annotated with values for tasks of the plurality of tasks wherein the optimizing includes inferring prior distributions for the blocks of the matrix-variate Gaussian scaled mixture prior that induce sparseness of the plurality of blocks; andpredicting values of tasks of the plurality of tasks for an input set of feature values using the optimized Bayesian model.