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• W2118099552 abstract "Multi-task learning (MTL) learns multiple related tasks simultaneously to improve generalization performance. Alternating structure optimization (ASO) is a popular MTL method that learns a shared low-dimensional predictive structure on hypothesis spaces from multiple related tasks. It has been applied successfully in many real world applications. As an alternative MTL approach, clustered multi-task learning (CMTL) assumes that multiple tasks follow a clustered structure, i.e., tasks are partitioned into a set of groups where tasks in the same group are similar to each other, and that such a clustered structure is unknown a priori. The objectives in ASO and CMTL differ in how multiple tasks are related. Interestingly, we show in this paper the equivalence relationship between ASO and CMTL, providing significant new insights into ASO and CMTL as well as their inherent relationship. The CMTL formulation is non-convex, and we adopt a convex relaxation to the CMTL formulation. We further establish the equivalence relationship between the proposed convex relaxation of CMTL and an existing convex relaxation of ASO, and show that the proposed convex CMTL formulation is significantly more efficient especially for high-dimensional data. In addition, we present three algorithms for solving the convex CMTL formulation. We report experimental results on benchmark datasets to demonstrate the efficiency of the proposed algorithms." @default.
• W2118099552 created "2016-06-24" @default.
• W2118099552 creator A5010419481 @default.
• W2118099552 creator A5047215778 @default.
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• W2118099552 date "2011-01-01" @default.
• W2118099552 modified "2023-09-23" @default.
• W2118099552 title "Clustered Multi-Task Learning Via Alternating Structure Optimization." @default.
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• W2118099552 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/4200604" @default.
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• W2118099552 hasPublicationYear "2011" @default.
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