document: Extracting shared subspace for multi-label classification Multi-label problems arise in various domains such as multi-topic document categorization and protein function prediction . One natural way to deal with such problems is to construct a binary classifier for each label , resulting in a set of independent binary classification problems . Since the multiple labels share the same input space , and the semantics 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 extracting 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 non-convex . 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 framework is that it includes several well-known algorithms as special cases , thus elucidating their intrinsic relationships . We have conducted extensive experiments on eleven multi-topic web page categorization tasks , and results demonstrate the effectiveness of the proposed formulation in comparison with several representative algorithms .
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