Joint Feature Selection and Structure Preservation for Domain Adaptation

The essence of domain adaptation is to explore common latent factors shared by the involved domains. These factors can be specific features or geometric structures. Most of previous methods exploit either the shared features or the shared geometric structures separately. However, the two strategies are complementary with each other and jointly exploring them is more optimal. This paper proposes a novel approach, named joint Feature Selection and Structure Preservation (FSSP), for unsupervised domain adaptation. FSSP smoothly integrates structure preservation and feature selection into a unified optimization problem. Intensive experiments on text categorization, image classification and video event recognition demonstrate that our method performs better, even with up to 30% improvement in average, compared with the state-of-the-art methods.

[1]  Ivor W. Tsang,et al.  Domain Transfer SVM for video concept detection , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[2]  Xiaojin Zhu,et al.  Harmonic mixtures: combining mixture models and graph-based methods for inductive and scalable semi-supervised learning , 2005, ICML.

[3]  Jiawei Han,et al.  Joint Feature Selection and Subspace Learning , 2011, IJCAI.

[4]  Junbin Gao,et al.  Dual Graph Regularized Latent Low-Rank Representation for Subspace Clustering , 2015, IEEE Transactions on Image Processing.

[5]  Dacheng Tao,et al.  Bregman Divergence-Based Regularization for Transfer Subspace Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.

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

[7]  Rama Chellappa,et al.  Domain Adaptive Dictionary Learning , 2012, ECCV.

[8]  Fan Chung,et al.  Spectral Graph Theory , 1996 .

[9]  Stephen Lin,et al.  Graph Embedding and Extensions: A General Framework for Dimensionality Reduction , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Ming Shao,et al.  Deep Low-Rank Coding for Transfer Learning , 2015, IJCAI.

[11]  Shuicheng Yan,et al.  Robust and Efficient Subspace Segmentation via Least Squares Regression , 2012, ECCV.

[12]  Trevor Darrell,et al.  DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition , 2013, ICML.

[13]  Feiping Nie,et al.  Efficient and Robust Feature Selection via Joint ℓ2, 1-Norms Minimization , 2010, NIPS.

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

[15]  Jiawei Han,et al.  Spectral Regression: A Unified Approach for Sparse Subspace Learning , 2007, Seventh IEEE International Conference on Data Mining (ICDM 2007).

[16]  Peter Kulchyski.  and , 2015 .

[17]  Ke Lu,et al.  Multi-manifold Sparse Graph Embedding for Multi-modal Image Classification , 2016, Neurocomputing.

[18]  G. Sapiro,et al.  A collaborative framework for 3D alignment and classification of heterogeneous subvolumes in cryo-electron tomography. , 2013, Journal of structural biology.

[19]  Yi Ma,et al.  The Augmented Lagrange Multiplier Method for Exact Recovery of Corrupted Low-Rank Matrices , 2010, Journal of structural biology.

[20]  Gunnar Rätsch,et al.  An Empirical Analysis of Domain Adaptation Algorithms for Genomic Sequence Analysis , 2008, NIPS.

[21]  M. Hestenes.  Multiplier and gradient methods , 1969 .

[22]  Ivor W. Tsang,et al.  Visual Event Recognition in Videos by Learning from Web Data , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[23]  Ke Lu,et al.  Sparse-Representation-Based Graph Embedding for Traffic Sign Recognition , 2012, IEEE Transactions on Intelligent Transportation Systems.

[24]  Dong Xu,et al.  Exploiting web images for event recognition in consumer videos: A multiple source domain adaptation approach , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[25]  Qiang Yang,et al.  A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.

[26]  M. Turk,et al.  Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.

[27]  Jiawei Han,et al.  Knowledge transfer via multiple model local structure mapping , 2008, KDD.

[28]  Fernando De la Torre,et al.  Selective Transfer Machine for Personalized Facial Action Unit Detection , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

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

[30]  G. Griffin,et al.  Caltech-256 Object Category Dataset , 2007 .

[31]  G. Saridis,et al.  Journal of Optimization Theory and Applications Approximate Solutions to the Time-invariant Hamilton-jacobi-bellman Equation 1 , 1998 .

[32]  Yun Fu,et al.  Low-Rank Common Subspace for Multi-view Learning , 2014, 2014 IEEE International Conference on Data Mining.

[33]  Philip S. Yu,et al.  Transfer Joint Matching for Unsupervised Domain Adaptation , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[34]  Jianmin Wang,et al.  Transfer Learning with Graph Co-Regularization , 2012, IEEE Transactions on Knowledge and Data Engineering.

[35]  Lorenzo Bruzzone,et al.  Domain Adaptation Problems: A DASVM Classification Technique and a Circular Validation Strategy , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[36]  Shih-Fu Chang,et al.  Consumer video understanding: a benchmark database and an evaluation of human and machine performance , 2011, ICMR.