Abstract:
In this paper, we consider the problem of learning multiple related tasks simultaneously with the goal of improving the generalization performance of individual tasks. Th...Show MoreMetadata
Abstract:
In this paper, we consider the problem of learning multiple related tasks simultaneously with the goal of improving the generalization performance of individual tasks. The key challenge is to effectively exploit the shared information across multiple tasks as well as preserve the discriminative information for each individual task. To address this, we propose a novel probabilistic model for multitask learning (MTL) that can automatically balance between low-rank and sparsity constraints. The former assumes a low-rank structure of the underlying predictive hypothesis space to explicitly capture the relationship of different tasks and the latter learns the incoherent sparse patterns private to each task. We derive and perform inference via variational Bayesian methods. Experimental results on both regression and classification tasks on real-world applications demonstrate the effectiveness of the proposed method in dealing with the MTL problems.
Published in: IEEE Transactions on Neural Networks and Learning Systems ( Volume: 29, Issue: 3, March 2018)
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- IEEE Keywords
- Index Terms
- Multi-task Learning ,
- Information Exchange ,
- Classification Task ,
- Generalization Performance ,
- Multiple Tasks ,
- Individual Tasks ,
- Variational Inference ,
- Sparsity Constraint ,
- Sparsity Pattern ,
- Hypothesis Space ,
- Low-rank Structure ,
- Low-rank Constraint ,
- Bayesian Model ,
- Latent Variables ,
- Diagonal Matrix ,
- Learning Task ,
- Singular Value ,
- Transfer Learning ,
- Bayesian Framework ,
- Binary Matrix ,
- Sparse Component ,
- Yeast Dataset ,
- Multi-label Learning ,
- Multi-task Learning Method ,
- Distribution In Order ,
- Sparsity Level ,
- Lasso Method ,
- Conjugate Prior ,
- Sparse Feature ,
- Multi-label Classification Task
- Author Keywords
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Multi-task Learning ,
- Information Exchange ,
- Classification Task ,
- Generalization Performance ,
- Multiple Tasks ,
- Individual Tasks ,
- Variational Inference ,
- Sparsity Constraint ,
- Sparsity Pattern ,
- Hypothesis Space ,
- Low-rank Structure ,
- Low-rank Constraint ,
- Bayesian Model ,
- Latent Variables ,
- Diagonal Matrix ,
- Learning Task ,
- Singular Value ,
- Transfer Learning ,
- Bayesian Framework ,
- Binary Matrix ,
- Sparse Component ,
- Yeast Dataset ,
- Multi-label Learning ,
- Multi-task Learning Method ,
- Distribution In Order ,
- Sparsity Level ,
- Lasso Method ,
- Conjugate Prior ,
- Sparse Feature ,
- Multi-label Classification Task
- Author Keywords