Papers Since 2008

Papers Before 2008

Journal Papers

• D.Y. Yeung, H. Chang, G. Dai. Learning the kernel matrix by maximizing a KFD-based class separability criterion. Pattern Recognition, 40(7):2021-2028, July 2007.

• H. Chang, D.Y. Yeung. Kernel-based distance metric learning for content-based image retrieval. Image and Vision Computing, 25(5):695-703, May 2007.

• D.Y. Yeung, H. Chang. A kernel approach for semi-supservised metric learning. IEEE Transactions on Neural Networks, 18(1):141-149, January 2007.

• H. Chang, D.Y. Yeung, W.K. Cheung. Relaxational metric adaptation and its application to semi-supervised clustering and content-based image retrieval. Pattern Recognition, 39(10):1905-1917, October 2006.

• H. Chang, D.Y. Yeung. Locally linear metric adaptation with application to semi-supervised clustering and image retrieval. Pattern Recognition, 39(7):1253-1264, July 2006.

• H. Chang, D.Y. Yeung. Robust locally linear embedding. Pattern Recognition, 39(6):1053-1065, June 2006.

• D.Y. Yeung, H. Chang. Extending the relevant component analysis algorithm for metric learning using both positive and negative equivalence constraints. Pattern Recognition, 39(5):1007-1010, May 2006.

Book Chapters

• H. Chang, D.Y. Yeung. Kernel-based metric adaptation with pairwise constraints. In Advances in Machine Learning and Cybernetics, D.S. Yeung, Z.Q. Liu, X.Z. Wang & H. Yan (eds.), pp.721-730, Springer-Verlag, 2006.

Conference Papers

• H. Chang, D.Y. Yeung. Locally smooth metric learning with application to image retrieval.  Proceedings of the Eleventh IEEE International Conference on Computer Vision (ICCV) , Rio de Janeiro, Brazil, 14-20 October 2007.

• D.Y. Yeung, H. Chang, G. Dai. A scalable kernel-based algorithm for semi-supervised metric learning.  Proceedings of the Twentieth International Joint Conference on Artificial Intelligence (IJCAI) , Hyderabad, India, 6-12 January, 2007.

• J.J. Pan, Q. Yang, H. Chang, D.Y. Yeung. A manifold regularization approach to calibration reduction for sensor-network-based tracking.  Proceedings of the Twenty-First National Conference on Artificial Intelligence (AAAI) , Boston, Massachusetts, USA, 16-20 July 2006.

• H. Chang, D.Y. Yeung. Graph Laplacian kernels for object classification from a single example.  Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) , New York, NY, USA, 17-22 June 2006.

• G. Dai, D.Y. Yeung, H. Chang. Extending kernel Fisher discriminant analysis with the weighted pairwise Chernoff criterion.  Proceedings of the Ninth European Conference on Computer Vision (ECCV) , Graz, Austria, 7-13 May 2006.

• H. Chang, D.Y. Yeung. Robust path-based spectral clustering with application to image segmentation.  Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV) . Beijing, China. 15-21 October 2005. 

• H. Chang, D.Y. Yeung. Stepwise metric adaptation based on semi-supervised learning for boosting image retrieval performance.  Proceedings of the Sixteenth British Machine Vision Conference (BMVC) . Oxford, UK. 5-8 September 2005.

• H. Chang, D.Y. Yeung. Semi-supervised metric learning by kernel matrix adaptation.  Proceedings of the Fourth International Conference on Machine Learning and Cybernetics (ICMLC) . Guangzhou, China. 18-21 August 2005. 

• H. Chang, D.Y. Yeung. Locally linear metric adaptation with application to image retrieval.  The 6th ACM Postgraduate Research Day , 12 March 2005. (Merit Award)

• D.Y. Yeung, H. Chang, Y. Xiong, S. George, R. Kashi, T. Matsumoto, G. Rigoll. SVC2004: First International Signature Verification Competition.  Proceedings of the International Conference on Biometric Authentication (ICBA) , Hong Kong, 15-17 July 2004.

• H. Chang, D.Y. Yeung. Locally linear metric adaptation for semi-supervised clustering.  Proceedings of the Twenty-First International Conference on Machine Learning (ICML) , pp.153-160, Banff, Alberta, Canada, 4-8 July 2004.

• H. Chang, D.Y. Yeung, Y. Xiong. Super-resolution through neighbor embedding.  Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) , vol.1, pp.275-282, Washington, DC, USA, 27 June - 2 July 2004. (Matlab code)

• H. Chang, D.Y. Yeung. Robust locally linear embedding.  The 5th ACM Postgraduate Research Day , 31 January 2004. (Merit Award)

Technical Reports and Previous Papers

• H. Chang. Semi-supervised distance metric learning. HKUST PhD thesis. January 2006.

• H. Chang, D.Y. Yeung. Robust locally linear embedding. Technical Report HKUST-CS05-12, Department of Computer Science, Hong Kong University of Science and Technology, July 2005.

• H. Chang. Some research problems in metric learning and manifold learning. HKUST PhD thesis proposal. November 2004.

• H. Chang, D.Y. Yeung. Robust path-based clustering for the unsupervised and semi-supervised learning settings. Technical Report HKUST-CS04-04, Department of Computer Science, Hong Kong University of Science and Technology, April 2004.

• D.Y. Yeung, H. Chang. Relaxational metric adaptation: a nonparametric approach to distance metric learning for clustering with pairwise side information. Technical Report HKUST-CS03-07, Department of Computer Science, Hong Kong University of Science and Technology, June 2003.

• H. Chang. A survey of model based clustering algorithms for sequential data. HKUST PhD qualifying examination. December 2002.

• P. He, H. Chang, M. Tian. Coupled chaotic dynamical system and the solution for gambling game problems. Proceedings of the International Joint Conference on Neural Networks (IJCNN), pp. 2564-2567, Washington, DC, July 15-19, 2001.

• H. Chang, P. He. The Combination of Neural Networks and Fuzzy Technique. Application Research of Computers, March, 2001. (in Chinese)

• H. Chang, P. He. Time Lagged Recurrent Neural Networks and the Applications in Forecasting of Stock Market. Academic Journey of Nanjing University, October, 2000. (in Chinese)
  　

Last updated December,2009