Dual Transfer Learning

Long, Mingsheng; Wang, Jianmin; Ding, Guiguang; Wei, Cheng; Zhang, Xiang; Wang, Wei

doi:10.1137/1.9781611972825.47

Cited by 60 publications

(36 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Such ranking averaging has been studied intensively for a long history [3,2,6,4,11]. The dual transfer learning methodology is first proposed in [12], which studies the strategy in the context of classification, while this work proposes a novel dual transfer strategy for the rating bias correction problem.…”

Section: Resultsmentioning

confidence: 99%

Robust crowd bias correction via dual knowledge transfer from multiple overlapping sources

Xie

Zhang

et al. 2015

2015 IEEE International Conference on Big Data (Big Data)

View full text Add to dashboard Cite

One of the largest constituents of big data is the crowdsourced or user-generated data which contain a wide range of valuable information. However, they are inherently biased and possibly spammed, making trustworthy information extraction an imperative task. As a special case, we study reviewer-posted ratings for products. The biased ratings can lead to disappointed customers due to overrated products, and reduced revenues of business owners caused by undeserved negative ratings. To distill objective product quality measurements, most existing methods try to infer unbiased ratings from the raw ratings alone, and may not overcome the inherent bias to recover the underlying true ratings. Though improved bias corrections have been achieved with domain expert helps, the overhead of expert efforts can be rather expensive in practice. We exploit the variety of big data and adopt a multiple source mining approach, which finds trustworthy measurements without domain expert, but with knowledge crowdsourced and transferred from external domains. We address the challenges that the multiple data sources are 1) inherently heterogeneous, 2) at most only partially overlapping and 3) biased by themselves. We explore and analyze the strengths and weaknesses of various knowledge transfer strategies. We then propose Consensus Ranking Dual Transfer (CRDT) to handle the above challenges by identifying "anchor reviewers" as a bridge for robust "dual transfer", and removing bias in individual sources via consensus ranking aggregation. Experiments on real-world rating datasets demonstrate that the proposed approach can deliver more robust bias correcting effects than the baselines and can identify abnormal reviewers.

show abstract

Section: Resultsmentioning

confidence: 99%

Robust crowd bias correction via dual knowledge transfer from multiple overlapping sources

Xie

Zhang

et al. 2015

2015 IEEE International Conference on Big Data (Big Data)

View full text Add to dashboard Cite

show abstract

“…The authors first introduce a linear transformation that maps features from the target to the source domain and then propose learning the transformation and the classifier parameters jointly. Another interesting work is presented in [14] where the concept of dual transfer learning is introduced. In [15] a metric learning approach for domain adaptation is proposed.…”

Section: Transfer Learningmentioning

confidence: 99%

Transferring knowledge across robots: A risk sensitive approach

Ciarfuglia

Valigi

Ricci

2015

Robotics and Autonomous Systems

View full text Add to dashboard Cite

“…The collective matrix tri-factorization based methods [19,21,14,12] can be categorized into the relation based transfering. Most of them share the associations between the word clusters and the document clusters across different domains.…”

Section: Related Workmentioning

confidence: 99%

“…Many existing transfer learning methods explore common latent factors shared by both domains to reduce the distribution divergence and bridge the gap between different domains [3,13,17,5]. Many of the transfer learning algorithms which are based on the collective matrix tri-factorization achieve a remarkable succuss in the recent literature [19,21,14,12]. This paper focuses on the literature of collective matrix factorization based transfer learning.…”

Section: Introductionmentioning

confidence: 99%

Discriminative Transfer Learning on Manifold

Fang

Zhang

2013

Proceedings of the 2013 SIAM International Conference on Data Mining

View full text Add to dashboard Cite

Collective matrix factorization has achieved a remarkable success in document classification in the literature of transfer learning. However, the learned latent factors still suffer from the divergence between different domains and thus are usually not discriminative for an appropriate assignment of category labels. Based on these observations, we impose a discriminative regression model over the latent factors to enhance the capability of label prediction. Moreover, we propose to minimize the Maximum Mean Discrepancy in the latent manifold subspace, as opposed to typically in the original data space, to bridge the gap between different domains. Specifically, we formulate these objectives into a joint optimization framework with two matrix tri-factorizations for the source and target domains simultaneously. An iterative algorithm DTLM is developed and the theoretical analysis of its convergence is discussed. Empirical study on benchmark datasets validates that DTLM improves the classification accuracy consistently compared with the state-of-theart transfer learning methods.

show abstract

Dual Transfer Learning

Cited by 60 publications

References 18 publications

Robust crowd bias correction via dual knowledge transfer from multiple overlapping sources

Robust crowd bias correction via dual knowledge transfer from multiple overlapping sources

Transferring knowledge across robots: A risk sensitive approach

Discriminative Transfer Learning on Manifold

Contact Info

Product

Resources

About