Learning Discriminative Correlation Subspace for Heterogeneous Domain Adaptation
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Author / Producer
Date
2017
Publication Type
Conference Paper
ETH Bibliography
yes
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Abstract
Domain adaptation aims to reduce the effort on collecting and annotating target data by leveraging knowledge from a different source domain. The domain adaptation problem will become extremely challenging when the feature spaces of the source and target domains are different, which is also known as the heterogeneous domain adaptation (HDA) problem. In this paper, we propose a novel HDA method to find the optimal discriminative correlation subspace for the source and target data. The discriminative correlation subspace is inherited from the canonical correlation subspace between the source and target data, and is further optimized to maximize the discriminative ability for the target domain classifier. We formulate a joint objective in order to simultaneously learn the discriminative correlation subspace and the target domain classifier. We then apply an alternating direction method of multiplier (ADMM) algorithm to address the resulting non-convex optimization problem. Comprehensive experiments on two real-world data sets demonstrate the effectiveness of the proposed method compared to the state-of-the-art methods.
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published
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Book title
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17)
Journal / series
Volume
Pages / Article No.
3252 - 3258
Publisher
International Joint Conferences on Artificial Intelligence
Event
26th International Joint Conference on Artificial Intelligence (IJCAI 2017)
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Methods
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Date created
Subject
Machine Learning: Classification; Machine Learning: Transfer, Adaptation, Multi-task Learning
Organisational unit
03514 - Van Gool, Luc (emeritus) / Van Gool, Luc (emeritus)