We study the problem of active learning for multilabel classification.
\nWe focus on the real-world scenario where the
\naverage number of positive (relevant) labels per data point
\nis small leading to positive label sparsity. Carrying out mutual
\ninformation based near-optimal active learning in this
\nsetting is a challenging task since the computational complexity
\ninvolved is exponential in the total number of labels.
\nWe propose a novel inference algorithm for the sparse
\nBayesian multilabel model of [17]. The benefit of this alternate
\ninference scheme is that it enables a natural approximation
\nof the mutual information objective. We prove that
\nthe approximation leads to an identical solution to the exact
\noptimization problem but at a fraction of the optimization
\ncost. This allows us to carry out efficient, non-myopic, and
\nnear-optimal active learning for sparse multilabel classification.
\nExtensive experiments reveal the effectiveness of the
\nmethod.<\/p>\n","protected":false},"excerpt":{"rendered":"
We study the problem of active learning for multilabel classification. We focus on the real-world scenario where the average number of positive (relevant) labels per data point is small leading to positive label sparsity. Carrying out mutual information based near-optimal active learning in this setting is a challenging task since the computational complexity involved is 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