Cross domain distribution adaptation via kernel mapping

Zhong, Erheng; Wang, Fan; Peng, Jing; Zhang, Kun; Ren, Jiangtao; Turaga, Deepak S.; Verscheure, Olivier

doi:10.1145/1557019.1557130

Cited by 103 publications

(37 citation statements)

References 17 publications

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“…Experiments are performed for muscle fatigue classification using surface electromyography data where classification accuracy is measured as the performance metric. Each source domain [143], Gao [37], and Duan [29]. The order of performance from best to worst is 2SW-MDA, CP-MDA, Duan [29], Zhong [143], Gao [37], Pan [87], Huang [51], and the baseline approach.…”

Section: Instance-based Transfer Learningmentioning

confidence: 99%

A survey of transfer learning

2016

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Section: Instance-based Transfer Learningmentioning

confidence: 99%

A survey of transfer learning

2016

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“…Instance-based approaches [10,20] emphasize sample selection or reweighting of the source data according to their difference from the target data. Kernel-based feature mapping with ensemble (KMapEnsemble) [21] is an adaptive kernel-and sample-based method that maps the marginal distribution of the source and target data into a shared space, and exploits a sample selection method to reduce conditional distribution across domains. Our proposed method has essential differences from KMapEnsemble.…”

Section: Related Workmentioning

confidence: 99%

Exploiting kernel-based feature weighting and instance clustering to transfer knowledge across domains

Tahmoresnezhad¹,

Hashemi²

2017

Turk J Elec Eng & Comp Sci

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Learning invariant features across domains is of vital importance to unsupervised domain adaptation, whereclassifiers trained on the training examples (source domain) need to adapt to a different set of test examples (target domain) in which no labeled examples are available. In this paper, we propose a novel approach to find the invariant features in the original space and transfer the knowledge across domains. We extract invariant features of input data by a kernel-based feature weighting approach, which exploits distribution difference and instance clustering to find desired features. The proposed method is called the kernel-based feature weighting (KFW) approach and benefits from the maximum mean discrepancy to measure the difference between domains. KFW uses condensed clusters in the reduced domains, the domains that do not contain variant features, to enhance the classification performance. Simultaneous use of feature weighting and instance clustering increases the adaptation and classification performance. Our approach automatically discovers the invariant features across domains and employs them to bridge between source and target domains. We demonstrate the effectiveness of our approach in the task of artificial and real world dataset examinations.Empirical results show that the proposed method outperforms other state-of-the-art methods on the standard transfer learning benchmark datasets.

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“…Two representative techniques for transfer learning are instance weighting [5], which extends Adaboost to filter those useless source domain data, and feature mapping [15,19] which transfers knowledge across domains through kernel based dimension reduction. However, these traditional transfer learning approaches focus on addressing the distribution shift across domains but work with a single source domain under a single-view.…”

Section: Related Workmentioning

confidence: 99%

Multi-Transfer: Transfer Learning with Multiple Views and Multiple Sources

Tan

Xiang

Yang

et al. 2013

Proceedings of the 2013 SIAM International Conference on Data Mining

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Transfer learning, which aims to help the learning task in a target domain by leveraging knowledge from auxiliary domains, has been demonstrated to be effective in different applications, e.g., text mining, sentiment analysis, etc. In addition, in many real-world applications, auxiliary data are described from multiple perspectives and usually carried by multiple sources. For example, to help classify videos on Youtube, which include three views/perspectives: image, voice and subtitles, one may borrow data from Flickr, Last.FM and Google News. Although any single instance in these domains can only cover a part of the views available on Youtube, actually the piece of information carried by them may compensate with each other. In this paper, we define this transfer learning problem as Transfer Learning with Multiple Views and Multiple Sources. As different sources may have different probability distributions and different views may be compensate or inconsistent with each other, merging all data in a simplistic manner will not give optimal result. Thus, we propose a novel algorithm to leverage knowledge from different views and sources collaboratively, by letting different views from different sources complement each other through a co-training style framework, while revise the distribution differences in different domains. We conduct empirical studies on several real-world datasets to show that the proposed approach can improve the classification accuracy by up to 8% against different state-of-the-art baselines.

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Cross domain distribution adaptation via kernel mapping

Cited by 103 publications

References 17 publications

A survey of transfer learning

A survey of transfer learning

Exploiting kernel-based feature weighting and instance clustering to transfer knowledge across domains

Multi-Transfer: Transfer Learning with Multiple Views and Multiple Sources

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