NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
Machine Learning - Special issue on inductive transfer
Regularized multi--task learning
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning sparse metrics via linear programming
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Information-theoretic metric learning
Proceedings of the 24th international conference on Machine learning
Convex multi-task feature learning
Machine Learning
Distance Metric Learning for Large Margin Nearest Neighbor Classification
The Journal of Machine Learning Research
GSML: A Unified Framework for Sparse Metric Learning
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Geometry preserving multi-task metric learning
Machine Learning
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Multi-task learning, referring to the joint training of multiple problems, can usually lead to better performance by exploiting the shared information across all the problems. On the other hand, metric learning, an important research topic, is however often studied in the traditional single task setting. Targeting this problem, in this paper, we propose a novel multi-task metric learning framework. Based on the assumption that the discriminative information across all the tasks can be retained in a low-dimensional common subspace, our proposed framework can be readily used to extend many current metric learning approaches for the multi-task scenario. In particular, we apply our framework on a popular metric learning method called Large Margin Component Analysis (LMCA) and yield a new model called multi-task LMCA (mtLMCA). In addition to learning an appropriate metric, this model optimizes directly on the transformation matrix and demonstrates surprisingly good performance compared to many competitive approaches. One appealing feature of the proposed mtLMCA is that we can learn a metric of low rank, which proves effective in suppressing noise and hence more resistant to over-fitting. A series of experiments demonstrate the superiority of our proposed framework against four other comparison algorithms on both synthetic and real data.