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Shiming Xiang, Feiping Nie, Changshui Zhang, "SemiSupervised Classification via Local Spline Regression," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 11, pp. 20392053, November, 2010.  
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@article{ 10.1109/TPAMI.2010.35, author = {Shiming Xiang and Feiping Nie and Changshui Zhang}, title = {SemiSupervised Classification via Local Spline Regression}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {32}, number = {11}, issn = {01628828}, year = {2010}, pages = {20392053}, doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2010.35}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
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TY  JOUR JO  IEEE Transactions on Pattern Analysis and Machine Intelligence TI  SemiSupervised Classification via Local Spline Regression IS  11 SN  01628828 SP2039 EP2053 EPD  20392053 A1  Shiming Xiang, A1  Feiping Nie, A1  Changshui Zhang, PY  2010 KW  Semisupervised classification KW  local spline regression KW  interactive image segmentation. VL  32 JA  IEEE Transactions on Pattern Analysis and Machine Intelligence ER   
[1] R.A. Adams, Sobolev Spaces. Academic Press, 1975.
[2] R.K. Ando and T. Zhang, "TwoView Feature Generation Model for Semisupervised Learning," Proc. Int'l Conf. Machine Learning, pp. 2532, 2007.
[3] M.F. Balcan, A. Blum, and K. Yang, "CoTraining and Expansion: Towards Bridging Theory and Practice," Proc. Advances in Neural Information Processing Systems, pp. 8996, 2004.
[4] M. Belkin, I. Matveeva, and P. Niyogi, "Regularization and SemiSupervised Learning on Large Graphs," Proc. Int'l Conf. Learning Theory, pp. 624638, 2004.
[5] M. Belkin and P. Niyogi, "Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering," Advances in Neural Information Processing Systems 14, pp. 585591, MIT Press, 2002.
[6] M. Belkin, P. Niyogi, and V. Sindhwani, "Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples," J. Machine Learning Research, vol. 7, pp. 23992434, 2006.
[7] M. Belkin and P. Niyogi, "Laplacian Eigenmaps for Dimensionality Reduction and Data Representation," Neural Computation., vol. 15, no. 6, pp. 13731396, 2003.
[8] A. Blake, C. Rother, M. Brown, P. Perez, and P. Torr, "Interactive Image Segmentation Using an Adaptive GMMRF Model," Proc. European Conf. Computer Vision, pp. 428441, 2004.
[9] A. Blum and S. Chawla, "Learning from Labeled and Unlabeled Data Using Graph Mincuts," Proc. Int'l Conf. Machine Learning, pp. 1926, 2001.
[10] A. Blum and T. Mitchell, "Combining Labeled and Unlabeled Data with CoTraining," Proc. Ann. Conf. Computational Learning Theory, pp. 92100, 1998.
[11] F. Bookstein, "Principal Warps: ThinPlate Splines and the Decomposition of Deformations," IEEE Trans. Pattern Analysis and Machine Learning, vol. 11, no. 6, pp. 567585, June 1989.
[12] Y.Y. Boykov and M.P. Jolly, "Interactive Graph Cuts for Optimal Boundary & Region Segmentation of Objects in nd Images," Proc. IEEE Int'l Conf. Computer Vision, pp. 105112, 2001.
[13] O. Chapelle, B. Schölkopf, and A. Zien, SemiSupervised Learning. MIT Press, 2006.
[14] N.V. Chawla and G. Karakoulas, "Learning from Labeled and Unlabeled Data: An Empirical Study across Techniques and Domains," J. Artificial Intelligence Research, vol. 23, pp. 331366, 2005.
[15] F.G. Cozman, I. Cohen, and M.C. Cirelo, "SemiSupervised Learning of Mixture Models," Proc. Int'l Conf. Machine Learning, pp. 99106, 2003.
[16] S. Dasgupta, M.L. Littman, and D. McAllester, "Pac Generalization Bounds for CoTraining," Advances in Neural Information Processing Systems 14, MIT Press, 2001.
[17] J. Duchon, "Splines Minimizing RotationInvariant SemiNorms in Sobolev Spaces," Constructive Theory of Functions of Several Variables, A. Dold and B. Eckmann, eds., pp. 85100, SpringerVerlag, 1977.
[18] A. Fujino, N. Ueda, and K. Saito, "A Hybrid Generative/Discriminative Approach SemiSupervised Classifier Design," Proc. Conf. Artificial Intelligence, pp. 764769, 2005.
[19] G. Getz, N. Shental, and E. Domany, "SemiSupervised Learning—A Statistical Physics Approach," Proc. ICML Workshop Learning with Partially Classified Training Data, 2005.
[20] S. Goldman and Y. Zhou, "Enhancing Supervised Learning with Unlabeled Data," Proc. Int'l Conf. Machine Learning, pp. 327334, 2000.
[21] G.H. Golub and C.F. van Loan, Matrix Computations, third ed. The Johns Hopkins Univ. Press, 1996.
[22] L. Grady, T. Schiwietz, S. Aharon, and R. Westermann, "Random Walks for Interactive AlphaMatting," Proc. Fifth IASTED Int'l Conf. Visualization, Imaging and Image Processing, pp. 423429, 2005.
[23] X. He and P. Niyogi, "Locality Preserving Projections," Proc. Ann. Conf. Neural Information Processing Systems, 2003.
[24] T. Joachims, "Transductive Learning via Spectral Graph Partitioning," Proc. Int'l Conf. Machine Learning, pp. 290297, 2003.
[25] T. Joachims, "Transductive Inference for Text Classification Using Support Vector Machines," Proc. Int'l Conf. Machine Learning, pp. 200209, 1999.
[26] I.T. Jolliffe, Principal Component Analysis, second ed. Springer, 2002.
[27] R. Jones, "Learning to Extract Entities from Labeled and Unlabeled Text," Technical Report CMULTI05191, Carnegie Mellon Univ., 2005.
[28] N.D. Lawrence and M.I. Jordan, "SemiSupervised Learning via Gaussian Processes," Advances in Neural Information Processing Systems 14, pp. 753760, MIT Press, 2004.
[29] A. Levin, D. Lischinski, and Y. Weiss, "A ClosedForm Solution to Natural Image Matting," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 30, no. 2, pp. 228242, Feb. 2008.
[30] A. Levin, A. RavAcha, and D. Lischinski, "Spectral Matting," Proc. Int'l Conf. Computer Vision and Pattern Recognition, pp. 18, 2007.
[31] Y. Li, J. Sun, C. Tang, and H. Shum, "Lazy Snapping," Proc. ACM SIGGRAPH, pp. 303308, 2004.
[32] D.R. Martin, C. Fowlkes, D. Tal, and J. Malik, "A Database of Human Segmented Natural Images and Its Application to Evaluating Segmentation Algorithms and Measuring Ecological Statistics," Proc. IEEE Int'l Conf. Computer Vision, pp. 416425, 2001.
[33] D.J. Miller and H.S. Uyar, "A Mixture of Experts Classifier with Learning Based on Both Labelled and Unlabelled Data," Advances in Neural Information Processing Systems 9, pp. 571577, MIT Press, 1996.
[34] K. Nigam and R. Ghani, "Analyzing the Effectiveness and Applicability of CoTraining," Proc. Int'l Conf. Information and Knowledge Management, pp. 8693, 2000.
[35] K. Nigam, A.K. McCallum, S. Thrun, and T.M. Mitchell, "Text Classification from Labeled and Unlabeled Documents Using EM," Machine Learning, vol. 39, nos. 2/3, pp. 103134, 2000.
[36] C. Rother, V. Kolmogorov, and A. Blake, "Grabcut—Interactive Foreground Extraction Using Iterated Graph Cuts," Proc. ACM SIGGRAPH, pp. 309314, 2004.
[37] L.K. Saul and S.T. Roweis, "Thinking Globally, Fit Locally: Unsupervised Learning of Low Dimensional Manifolds," J. Machine Learning Research, vol. 4, pp. 119155, 2003.
[38] B. Shahshahani and D. Landgrebe, "The Effect of Unlabeled Samples in Reducing the Small Sample Size Problem and Mitigating the Hughes Phenomenon," IEEE Trans. Geoscience and Remote Sensing, vol. 32, no. 5, pp. 10871095, Sept. 1994.
[39] J.B. Shi and J. Malik, "Normalized Cuts and Image Segmentation," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp. 888905, Aug. 2000.
[40] J. Sun, J. Jia, C.K. Tang, and H.Y. Shum, "Poisson Matting," ACM Trans. Graphics, vol. 23, no. 3, pp. 315321, 2004.
[41] M. Szummer and T. Jaakkola, "Partially Labeled Classification with Markov Random Walks," Advances in Neural Information Processing Systems 14, pp. 945952, MIT Press, 2001.
[42] G. Wahba, Spline Models for Observational Data. SIAM Press, 1990.
[43] C. Walder and O. Chapelle, "Learning with Transformation Invariant Kernels," Advances in Neural Information Processing Systems, pp. 15611568, MIT Press, 2007.
[44] F. Wang, S.J. Wang, C.S. Zhang, and O. Winther, "SemiSupervised Mean Fields," Proc. Int'l Conf. Artificial Intelligence and Statistics, pp. 596603, 2007.
[45] F. Wang, T. Li, G. Wang, and C. Zhang, "SemiSupervised Classification Using Local and Global Regularization," Proc. 23rd AAAI Conf. Artificial Intelligence, pp. 726731, 2008.
[46] F. Wang and C. Zhang, "Label Propagation through Linear Neighborhoods," IEEE Trans. Knowledge and Data Eng., vol. 20, no. 1, pp. 5567, Jan. 2008.
[47] F. Wang and C. Zhang, "On Discriminative SemiSupervised Classification," Proc. The 23rd AAAI Conf. Artificial Intelligence, pp. 720725, 2008.
[48] J. Wang and M.F. Cohen, "An Iterative Optimization Approach for Unified Image Segmentation and Matting," Proc. Int'l Conf. Computer Vision, pp. 936943, 2005.
[49] H. Wendland, Scattered Data Approximation. Cambridge Univ. Press, 2005.
[50] M. Wu, K. Yu, S. Yu, and B. Schölkopf, "Local Learning Projections," Proc. Int'l Conf. Machine Learning, pp. 10391046, 2007.
[51] M.R. Wu and B. Schölkopf, "A Local Learning Approach for Clustering," Advances in Neural Information Processing Systems 19, pp. 15291536, MIT Press, 2007.
[52] M.R. Wu and B. Schölkopf, "Transductive Classification via Local Learning Regularization," Proc. Int'l Conf. Artificial Intelligence and Statistics, pp. 628635, 2007.
[53] S.M. Xiang, F.P. Nie, C.S. Zhang, and C.X. Zhang, "Spline Embedding for Nonlinear Dimensionality Reduction," Proc. European Conf. Machine Learning, pp. 825832, 2006.
[54] J. Yoon, "Spectral Approximation Orders of Radial Basis Function Interpolation on the Sobolev Space," SIAM J. Math. Analysis, vol. 33, no. 4, pp. 946958, 2001.
[55] Z. Zhang and H. Zha, "Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment," SIAM J. Scientific Computing, vol. 26, no. 1, pp. 313338, 2004.
[56] D. Zhou, O. Bousquet, T. Lal, J. Weston, and B. Schölkopf, "Learning with Local and Global Consistency," Advances in Neural Information Processing Systems 16, pp. 321328, MIT Press, 2003.
[57] Z.H. Zhou and M. Li, "TriTraining: Exploiting Unlabeled Data Using Three Classifiers," IEEE Trans. Knowledge and Data Eng., vol. 17, no. 11, pp. 15291541, Nov. 2005.
[58] Z.H. Zhou, D.C. Zhan, and Q. Yang, "SemiSupervised Learning with Very Few Labeled Training Examples," Proc. Conf. Artificial Intelligence, pp. 675680, 2007.
[59] X.J. Zhu, Z. Ghahramani, and J. Lafferty, "SemiSupervised Learning Using Gaussian Fields and Harmonic Functions," Proc. Int'l Conf. Machine Learning, pp. 912919, 2003.
[60] X.J. Zhu, "SemiSupervised Learning Literature Survey," Technical Report Computer Sciences TR 1530, Univ. of WisconsinMadison, 2007.