Discriminative stochastic neighbor embedding analysis method

Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of Computer-Aided Design and Computer Graphics

Volumn 24, Issue 11, 2012, Pages 1477-1484

  Zheng, Jianwei ^a   Qiu, Hong ^a   Jiang, Yibo ^a   Wang, Wanliang ^a  

^a ZHEJIANG UNIVERSITY OF TECHNOLOGY   (China)

Author keywords

Data visualization; Manifold embedding; Stochastic neighbor embedding; Supervised learning

Indexed keywords

ANALYSIS METHOD; CLASS LABELS; DATA MANIFOLDS; FACE DATABASE; FEATURE EXTRACTION METHODS; INPUT SAMPLE; JOINT PROBABILITY; KULLBACK LEIBLER DIVERGENCE; LINEAR PROJECTIONS; MANIFOLD EMBEDDING; STOCHASTIC NEIGHBOR EMBEDDING; SYNTHETIC DATA;

ALGORITHMS; FEATURE EXTRACTION; SUPERVISED LEARNING;

DATA VISUALIZATION;

EID: 84870740348     PISSN: 10039775     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (19)

1. Turk M, Pentland A. Eigenfaces for recognition[J]. Journal of Cognitive Neuroscience, 1991, 3(1): 71-86
2. Belhumeur P N, Hespanha J P, Kriegman D J. Eigenfaces vs. Fisherfaces: recognition using class specific linear projection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(7): 711-720
3. Yan S C, Xu D, Zhang B Y, et al. Graph embedding and extensions: a general framework for dimensionality reduction[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(1): 40-51
4. Sugiyama M. Dimensionality reduction of multimodal labeled data by local fisher discriminant analysis[J]. Journal of Machine Learning Research, 2007, 8(1): 1027-1061
5. Bian W, Tao D C. Max-min distance analysis by using sequential SDP relaxation for dimension reduction[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(5): 1037-1050
6. Wan Minghua, Jin Zhong. Local graph embedding feature extraction method based on maximum margin criterion[J]. Journal of Computer-Aided Design & Computer Graphics, 2011, 23(7): 1224-1231 (in Chinese)
7. Wu Feng, Zhong Yan, Wu Quanyuan. Online classification framework for data stream based on incremental kernel principal component analysis[J]. Acta Automatica Sinica, 2010, 36(4): 534-542(in Chinese)
8. Yang J, Frangi A F, Yang J Y, et al. KPCA plus LDA: a complete kernel fisher discriminant framework for feature extraction and recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(2): 230-244
9. Tenenbaum J B, Silva V, Langford J C. A global geometric framework for nonlinear dimensionality reduction[J]. Science, 2000, 290(5500): 2319-2323
10. Roweis S T, Lawrance K S. Nonlinear dimensionality reduction by locally linear embedding[J]. Science, 2000, 290(5500): 2323-2326
11. Belkin M, Niyogi P. Laplacian eigenmaps for dimensionality reduction and data representation[J]. Neural Computation, 2003, 15(6): 1373-1396
12. Li H, Jiang H, Barrio R, et al. Incremental manifold learning by spectral embedding methods[J]. Pattern Recognition Letters, 2011, 32(10): 1447-1455
13. Zhang P, Qiao H, Zhang B. An improved local tangent space alignment method for manifold learning[J]. Pattern Recognition Letters, 2011, 23(2): 181-189
14. Zhang T H, Huang K Q, Li X L, et al. Discriminative orthogonal neighborhood-preserving projections for classification[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 2010, 40(1): 253-263
15. He X F, Yan S C, Hu Y X, et al. Face recognition using Laplacian faces[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(3): 328-340
16. Zhang T, Yang J, Zhao D. Linear local tangent space alignment and application to face recognition[J]. Neurocomputing, 2007, 70(7/9): 1547-1553
17. Hinton G, Roweis S. Stochastic neighbor embedding[J]. Advances in Neural Information Processing Systems, 2003, 15(3): 833-840
18. Van Der Maaten L, Hinton G. Visualizing data using t-SNE[J]. Journal of Machine Learning Research, 2008, 9(11): 2579-2605
19. Van Der Maaten L, Postma E O, Van Den Herik H. Dimensionality reduction: a comparative review[J]. Review Literature and Arts of the Americas, 2007, 10(2): 1-35