Unsupervised Visual Domain Adaptation Using Subspace Alignment

In this paper, we introduce a new domain adaptation (DA) algorithm where the source and target domains are represented by subspaces described by eigenvectors. In this context, our method seeks a domain adaptation solution by learning a mapping function which aligns the source subspace with the target one. We show that the solution of the corresponding optimization problem can be obtained in a simple closed form, leading to an extremely fast algorithm. We use a theoretical result to tune the unique hyper parameter corresponding to the size of the subspaces. We run our method on various datasets and show that, despite its intrinsic simplicity, it outperforms state of the art DA methods.

[1]  Gilles Blanchard,et al.  On the Convergence of Eigenspaces in Kernel Principal Component Analysis , 2005, NIPS.

[2]  Koby Crammer,et al.  Analysis of Representations for Domain Adaptation , 2006, NIPS.

[3]  John Blitzer,et al.  Domain Adaptation with Structural Correspondence Learning , 2006, EMNLP.

[4]  Qiang Yang,et al.  Transfer Learning via Dimensionality Reduction , 2008, AAAI.

[5]  S. Mahadevan,et al.  Manifold Alignment without Correspondence , 2009, IJCAI.

[6]  Ivor W. Tsang,et al.  Extracting discriminative concepts for domain adaptation in text mining , 2009, KDD.

[7]  Trevor Darrell,et al.  Adapting Visual Category Models to New Domains , 2010, ECCV.

[8]  Wen Gao,et al.  Manifold Alignment via Corresponding Projections , 2010, BMVC.

[9]  Alexei A. Efros,et al.  Unbiased look at dataset bias , 2011, CVPR 2011.

[10]  Anna Margolis,et al.  A Literature Review of Domain Adaptation with Unlabeled Data , 2011 .

[11]  Ivor W. Tsang,et al.  Domain Adaptation via Transfer Component Analysis , 2009, IEEE Transactions on Neural Networks.

[12]  John Blitzer,et al.  Domain Adaptation with Coupled Subspaces , 2011, AISTATS.

[13]  Rama Chellappa,et al.  Domain adaptation for object recognition: An unsupervised approach , 2011, 2011 International Conference on Computer Vision.

[14]  Chang Wang,et al.  Heterogeneous Domain Adaptation Using Manifold Alignment , 2011, IJCAI.

[15]  Trevor Darrell,et al.  What you saw is not what you get: Domain adaptation using asymmetric kernel transforms , 2011, CVPR 2011.

[16]  Alexei A. Efros,et al.  Undoing the Damage of Dataset Bias , 2012, ECCV.

[17]  Yuan Shi,et al.  Geodesic flow kernel for unsupervised domain adaptation , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[18]  Dong Liu,et al.  Robust visual domain adaptation with low-rank reconstruction , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[19]  Ruth Urner,et al.  Domain adaptation–can quantity compensate for quality? , 2013, Annals of Mathematics and Artificial Intelligence.