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A Shared-Subspace Learning Framework for Multi-Label Classification
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资源说明：Multi-label problems arise in various domains such as multi-topic document categorization, pro-
tein function prediction, and automatic image annotation. One natural way to deal with such
problems is to construct a binary classifier for each label, resulting in a set of independent bi-
nary classification problems. Since multiple labels share the same input space, and the seman-
tics conveyed by different labels are usually correlated, it is essential to exploit the correlation
information contained in different labels. In this paper, we consider a general framework for ex-
tracting shared structures in multi-label classification. In this framework, a common subspace is
assumed to be shared among multiple labels. We show that the optimal solution to the proposed
formulation can be obtained by solving a generalized eigenvalue problem, though the problem is
nonconvex. For high-dimensional problems, direct computation of the solution is expensive, and
we develop an efficient algorithm for this case. One appealing feature of the proposed frame-
work is that it includes several well-known algorithms as special cases, thus elucidating their
intrinsic relationships. We further show that the proposed framework can be extended to the
kernel-induced feature space. We have conducted extensive experiments on multi-topic web page
categorization and automatic gene expression pattern image annotation tasks, and results demon-
strate the effectiveness of the proposed formulation in comparison with several representative
algorithms.
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