Learning Task Clusters via Sparsity Grouped Multitask Learning

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Sparse mapping has been a key methodology in many high-dimensional scientific problems. When multiple tasks share the set of relevant features, learning them jointly in a group drastically improves the quality of relevant feature selection. However, in practice this technique is used limitedly since such grouping information is usually hidden. In this paper, our goal is to recover the group structure on the sparsity patterns and leverage that information in the sparse learning. Toward this, we formulate a joint optimization problem in the task parameter and the group membership, by constructing an appropriate regularizer to encourage sparse learning as well as correct recovery of task groups. We further demonstrate that our proposed method recovers groups and the sparsity patterns in the task parameters accurately by extensive experiments.
Publisher
Springer Verlag
Issue Date
2017-09
Language
English
Citation

European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2017, pp.673 - 689

ISSN
0302-9743
DOI
10.1007/978-3-319-71246-8_41
URI
http://hdl.handle.net/10203/310812
Appears in Collection
AI-Conference Papers(학술대회논문)
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