Flexible Clustered Multi-Task Learning by Learning Representative Tasks

Multi-task learning (MTL) methods have shown promising performance by learning multiple relevant tasks simultaneously, which exploits to share useful information across relevant tasks. Among various MTL methods, clustered multi-task learning (CMTL) assumes that all tasks can be clustered into groups...

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Vydané v:IEEE transactions on pattern analysis and machine intelligence Ročník 38; číslo 2; s. 266 - 278
Hlavní autori: Zhou, Qiang, Zhao, Qi
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States IEEE 01.02.2016
Predmet:
Clustered Multi-Task Learning
Covariance matrices
Group Sparsity
Kernel
Learning systems
Optimization
Representative Task
Robustness
Training data
Visualization
representative task
group sparsity
Clustered multi-task learning
ISSN:0162-8828, 1939-3539, 2160-9292, 1939-3539
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Shrnutí:Multi-task learning (MTL) methods have shown promising performance by learning multiple relevant tasks simultaneously, which exploits to share useful information across relevant tasks. Among various MTL methods, clustered multi-task learning (CMTL) assumes that all tasks can be clustered into groups and attempts to learn the underlying cluster structure from the training data. In this paper, we present a new approach for CMTL, called flexible clustered multi-task (FCMTL), in which the cluster structure is learned by identifying representative tasks. The new approach allows an arbitrary task to be described by multiple representative tasks, effectively soft-assigning a task to multiple clusters with different weights. Unlike existing counterpart, the proposed approach is more flexible in that (a) it does not require clusters to be disjoint, (b) tasks within one particular cluster do not have to share information to the same extent, and (c) the number of clusters is automatically inferred from data. Computationally, the proposed approach is formulated as a row-sparsity pursuit problem. We validate the proposed FCMTL on both synthetic and real-world data sets, and empirical results demonstrate that it outperforms many existing MTL methods.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 23
ISSN:0162-8828
1939-3539
2160-9292
1939-3539
DOI:10.1109/TPAMI.2015.2452911