Abstract. We propose a manifold alignment based approach for heterogeneous domain adaptation. A key aspect of this approach is to construct mappings to link different feature spaces in order to transfer knowl- edge across domains. The new approach can reuse labeled data from multiple source domains in a tar-.

Abstract. We propose a manifold alignment based approach for heterogeneous domain adaptation. A key aspect of this approach is to construct mappings to link different feature spaces in order to transfer knowl- edge across domains. The new approach can reuse labeled data from multiple source domains in a tar-.

We propose a manifold alignment based approach for heterogeneous domain adaptation. A key aspect of this approach is to construct mappings to link different feature spaces in order to transfer knowledge across domains. The new approach can reuse labeled data from multiple source domains in a target domain even in ...

We propose a manifold alignment based approach for heterogeneous domain adaptation. A key aspect of this approach is to construct mappings to link different feature spaces in order to transfer knowledge across domains. The new approach can reuse labeled data from multiple source domains in a target domain even in ...

This paper extends existing manifold alignment approaches by making use of labels rather than correspondences to align the manifolds, which significantly broadens the application scope of manifold alignment. We propose a manifold alignment based approach for heterogeneous domain adaptation.

Learn mapping functions to project the source and target domains to a new latent space. • Matching the instances with the same labels. • Separating the instances with different labels. • Preserving the topology. 1) Source and target domains do not share any common features or instances.

Wang and Mahadevan. [38] chose to solve domain adaptation by manifold align- ment (DAMA), with the goal of preserving label informa- tion during their alignment/adaptation process. Duan et al. [12] proposed heterogeneous feature augmentation (HFA) for learning a common feature subspace, in which SVM.

19 hours ago · Specifically, we attempt to learn a domain-specific projection to project original samples into a common subspace in which the marginal distribution is well aligned and the discriminative knowledge consistency is preserved by leveraging the labeled samples from both domains.

We present a framework called TLF that builds a classifier for the target domain having only few labeled training records by transferring knowledge from the source domain having many labeled records. While existing methods often focus on one issue and leave the other one for the further work, TLF is capable of ...

Heterogeneous domain adaptation using manifold alignment. In IJCAI, pages 1541–1546, 2011. Jie Wang and Jieping Ye. Two-layer feature reduction for sparse-group lasso via decomposition of convex sets. In NIPS, pages 2132–2140, 2014. Meng Wang and Ao Tang. Conditions for a unique non-negative solution to an ...