摘要：Traditional machine learning methods share a common hypothesis: training and testing
datasets must be in a common feature space with the same distribution. However, in
reality, the labeled target data may be rare, so that target space does not share the same
feature space or distribution as an available training set (source domain). To address the
mismatch of domains, we propose a Dual-Subspace Transfer Learning (DSTL) framework
that considers both the common and specific information of the two domains. In DSTL,
a latent common subspace is first learned to preserve the data properties and reduce the
discrepancy of domains. Then, we propose a mapping strategy to transfer the sourcespecific
information to the target subspace. The integration of the domain-common and
specific information constructs the proposed DSTL framework. In comparison to the stateart-of
works, the main contribution of our work is that the DSTL framework not only
considers the commonalities, but also exploits the specific information. Experiments on
three emotional speech corpora verify the effectiveness of our approach. The results show
that the methods which include both domain-common and specific information perform
better than the baseline methods which only exploit the domain commonalities.
其他摘要：Traditional machine learning methods share a common hypothesis: training and testing datasets must be in a common feature space with the same distribution. However, in reality, the labeled target data may be rare, so that target space does not share the same feature space or distribution as an available training set (source domain). To address the mismatch of domains, we propose a Dual-Subspace Transfer Learning (DSTL) framework that considers both the common and specific information of the two domains. In DSTL, a latent common subspace is first learned to preserve the data properties and reduce the discrepancy of domains. Then, we propose a mapping strategy to transfer the sourcespecific information to the target subspace. The integration of the domain-common and specific information constructs the proposed DSTL framework. In comparison to the stateart-of works, the main contribution of our work is that the DSTL framework not only considers the commonalities, but also exploits the specific information. Experiments on three emotional speech corpora verify the effectiveness of our approach. The results show that the methods which include both domain-common and specific information perform better than the baseline methods which only exploit the domain commonalities.
