Graph embedding with constraints
作者： Ming Ji , Hujun Bao , Xiaofei He
DOI:
关键词: Complement graph 、 Graph embedding 、 Theoretical computer science 、 Voltage graph 、 Null graph 、 Topological graph theory 、 Strength of a graph 、 Machine learning 、 Artificial intelligence 、 Mathematics 、 Graph property 、 Graph (abstract data type)
摘要: Recently graph based dimensionality reduction has received a lot of interests in many fields information processing. Central to it is structure which models the geometrical and discriminant data manifold. When label available, usually incorporated into by modifying weights between points. In this paper, we propose novel algorithm, called Constrained Graph Embedding, considers as additional constraints. Specifically, constrain space solutions that explore only contain embedding results are consistent with labels. Experimental on two real life sets illustrate effectiveness our proposed method.
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uchicago.edu 参考文章(21)
Robert Tibshirani, Trevor Hastie, Jerome H. Friedman, The Elements of Statistical Learning ,(2001)
James Franklin, The elements of statistical learning : data mining, inference,and prediction The Mathematical Intelligencer. ,vol. 27, pp. 83- 85 ,(2005) , 10.1007/BF02985802
Fan R K Chung, Spectral Graph Theory ,(1996)
Joshua B Tenenbaum, Vin de Silva, John C Langford, A Global Geometric Framework for Nonlinear Dimensionality Reduction Science. ,vol. 290, pp. 2319- 2323 ,(2000) , 10.1126/SCIENCE.290.5500.2319
Terence Sim, S. Baker, M. Bsat, The CMU pose, illumination, and expression database IEEE Transactions on Pattern Analysis and Machine Intelligence. ,vol. 25, pp. 1615- 1618 ,(2003) , 10.1109/TPAMI.2003.1251154
Stephen Guattery, Gary L. Miller, Graph Embeddings and Laplacian Eigenvalues SIAM Journal on Matrix Analysis and Applications. ,vol. 21, pp. 703- 723 ,(2000) , 10.1137/S0895479897329825
Sam T Roweis, Lawrence K Saul, Nonlinear Dimensionality Reduction by Locally Linear Embedding Science. ,vol. 290, pp. 2323- 2326 ,(2000) , 10.1126/SCIENCE.290.5500.2323
Christopher C. Paige, Michael A. Saunders, LSQR: An Algorithm for Sparse Linear Equations and Sparse Least Squares ACM Transactions on Mathematical Software. ,vol. 8, pp. 43- 71 ,(1982) , 10.1145/355984.355989
Xiaofei He, Deng Cai, Shuicheng Yan, Hong-Jiang Zhang, Neighborhood preserving embedding international conference on computer vision. ,vol. 2, pp. 1208- 1213 ,(2005) , 10.1109/ICCV.2005.167
Masashi Sugiyama, Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis Journal of Machine Learning Research. ,vol. 8, pp. 1027- 1061 ,(2007)