Online Semi-Supervised Multi-Task Distance Metric Learning

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Conference Proceeding
IEEE International Conference on Data Mining Workshops, ICDMW, 2017, pp. 474 - 479
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© 2016 IEEE. Given several related tasks, multi-Task learning can improve the performance of each task through sharing parameters or feature representations. In this paper, we apply multi-Task learning to a particular case of distance metric learning, in which we have a small amount of labeled data. Consider the effectiveness of semi-supervised learning handling few labeled machine learning problems, we integrate semi-supervised learning with multi-Task learning and distance metric learning. One of the defect of multi-Task learning is its low training efficiency, as we need all the training examples from all tasks to train a model. We propose an online learning algorithm to overcome this drawback of multi-Task learning. Experiments are conducted on one landmark multi-Task learning dataset to demonstrate the efficiency and effectiveness of our online semi-supervised multi-Task learning algorithm.
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