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Binary domain adaptation with independence maximization
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DOI：10.1007/s13042-021-01339-z.png)
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摘要
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In the following study, an innovative domain adaptation technique is proposed. dCor based Domain Adaptation or dDA technique is based on maximum mean discrepancy (MMD) and distance correlation (dCor); a powerful yet general correlation measure which is applicable to arbitrary-dimensional random variables. By projecting the samples to a common latent feature space, dDA minimizes the discrepancy between the source and target distributions while preserving the structural information of the data. The proposed dDA algorithm can be easily implemented and it has a closed-form and simple solution. Extensive analyses across various real-world and sentiment analysis benchmark data sets indicate that our algorithm is the method of choice; as it offers superior results in comparison with several state-of-the-art domain adaptation approaches in the literature in both unsupervised and semi-supervised settings.
Keyword:
Transfer learning
Domain adaptation
Distance correlation (dCor)
Maximum mean discrepancy (MMD)
Sentiment analysis
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