Adaptive semi-supervised dimensionality reduction based on pairwise constraints weighting and graph optimizing

International Journal of Machine Learning and Cybernetics

Volumn 8, Issue 3, 2017, Pages 793-805

  Meng, Meng ^a   Wei, Jia ^a   Wang, Jiabing ^a   Ma, Qianli ^a   Wang, Xuan ^b  

^a SOUTH CHINA UNIVERSITY OF TECHNOLOGY   (China)

^b HARBIN INSTITUTE OF TECHNOLOGY   (China)

Author keywords

Adaptive dimensionality reduction; Graph construction optimizing; Pairwise constraints weighting; Semi supervised learning

Indexed keywords

CLUSTERING ALGORITHMS; DATA HANDLING; IMAGE RECOGNITION; SUPERVISED LEARNING;

DIMENSIONALITY REDUCTION; DIMENSIONALITY REDUCTION METHOD; DOMAIN KNOWLEDGE; GRAPH CONSTRUCTION; HIGH DIMENSIONAL DATA; LOW-DIMENSIONAL REPRESENTATION; PAIRWISE CONSTRAINTS; SEMI- SUPERVISED LEARNING;

DATA REDUCTION;

EID: 85019254461     PISSN: 18688071     EISSN: 1868808X     Source Type: Journal    
DOI: 10.1007/s13042-015-0380-3     Document Type: Article

References (31)

1. Duda RO, Hart PE, Stork DG (2001) Pattern classification, 2nd edn. Wiley, New York
2. Jolliffe IT (2002) Principal component analysis, 2nd edn. Wiley, New York
3. Sharma A, Paliwal KK, Imoto S, Miyano S (2013) Principal component analysis using QR decomposition. Int J Mach Learn Cybern (IJMLC) 4:679–683
4. Sharma A, Paliwal KK (2015) Linear discriminant analysis for the small sample size problem: an overview. Int J Mach Learn Cybern (IJMLC) 6:443–454
5. Chapelle O, Schölkopf B, Zien A (2006) Semi-supervised learning. MIT Press, Cambridge
6. Song Y, Nie F, Zhang C, Xiang S (2008) A unified framework for semi-supervised dimensionality reduction. Pattern Recognit 41(9):2789–2799
7. Chatpatanasiri R, Kijsirikul B (2010) A unified semi-supervised dimensionality reduction framework for manifold learning. Neurocomputing 73(10–12):1631–1640
8. Alok AK, Saha S, Ekbal A (2015) Semi-supervised clustering for gene-expression data in multiobjective optimization framework. Int J Mach Learn Cybern (IJMLC). doi:10.1007/s13042-015-0335-8
9. Wagstaff K, Cardie C (2000) Clustering with instance-level constraints. In: Proceedings of the 17th International Conference on Machine Learning, pp 1003–1110
10. Chen C, Zhang J, He X, Zhou ZH (2012) Non-parametric kernel learning with robust pairwise constraints. Int J Mach Learn Cybern (IJMLC) 3:83–96
11. Klein D, Kamvar SD, Manning CD (2002) From instance-level constraints to space-level constraints: making the most of prior knowledge in data clustering. In: Proceedings of the 19th International Conference on Machine Learning, pp 307–314
12. Bar-Hillel A, Hertz T, Shental N, Weinshall D (2005) Learning a mahalanobis metric from equivalence constraints. J Mach Learn Res 6:937–965
13. Xing EP, Ng AY, Jordan MI, Russell S (2003) Distance metric learning, with application to clustering with side-information. Adv Neural Inf Process Syst 15:505–512
14. Tang W, Zhong S (2006) Pairwise constraints-guided dimensionality reduction. In: Proceedings of the SDM’06 Workshop on Feature Selection for Data Mining, pp 59–66
15. Yeung DY, Chang H (2006) Extending the relevant component analysis algorithm for metric learning using both positive and negative equivalence constraints. Pattern Recognit 39(5):1007–1010
16. An S, Liu W, Venkatesh S (2008) Exploiting side information in locality preserving projection. In: Proceedings of the 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp 1–8
17. Zhang D, Zhou Z, Chen S (2007) Semi-supervised dimensionality reduction. In: Proceedings of the 7th SIAM International Conference on Data Mining, pp 629–634
18. Chen S, Zhang D (2011) Semisupervised dimensionality reduction with pairwise constraints for hyperspectral image classification. IEEE Geosci Remote Sens Lett 8(2):369–373
19. Cevikalp H, Verbeek J, Jurie F, Klaser A (2008) Semi-supervised dimensionality reduction using pairwise equivalence constraints. In: Proceedings of the 2008 International Conference on Computer Vision Theory and Applications, pp 489–496
20. He X, Niyogi P (2004) Locality preserving projections. Adv in Neural Inf Process Syst 16:153–160
21. Wei J, Peng H (2008) Neighborhood preserving based semi-supervised dimensionality reduction. Electron Lett 44(20):1190–1191
22. Roweis ST, Saul LK (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500):2323–2327
23. Baghshah MS, Shouraki SB (2009) Semi-supervised metric learning using pairwise constraints. In: Proceedings of the 21st International Joint Conference on Artificial Intelligence, pp 1217–1222
24. Davidson I (2009) Knowledge driven dimension reduction for clustering. In: Proceedings of the 21st International Joint Conference on Artificial Intelligence, pp 1034–1039
25. Yan S, Bouaziz S, Lee D, Barlow J (2012) Semi-supervised dimensionality reduction for analyzing high-dimensional data with constraints. Neurocomputing 76(1):114–124
26. Belkin M, Niyogi P (2003) Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput 15(6):1373–1396
27. Chapelle O, Zien A (2005) Semi-supervised classification by low density separation. In: Proceedings of the 22nd International Conference on Machine Learning, pp 57–64
28. Sim T, Barker S, Bsat M (2003) The CMU pose, illumination, and expression database. IEEE Trans Pattern Anal Mach Intell 25(12):1615–1618
29. Georghiades AS, Belhumeur PN, Kriegman DJ (2001) From few to many: illumination cone models for face recognition under variable lighting and pose. IEEE Trans Pattern Anal Mach Intell 23(6):643–660
30. LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324
31. Zhang D, Kong W, You J, Wong M (2003) Online palmprint identification. IEEE Trans Pattern Anal Mach Intell 25(9):1041–1050