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A novel discriminant minimum class locality preserving canonical correlation analysis and its applications
1. | Institute of Metrology and Computational Science, China Jiliang University, Hangzhou, 310018, Zhejiang Province, China, China, China |
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B. Abraham, et al., Dimensionality reduction approach to multivariate prediction,, Comput Stat Data Anal, 48 (2005), 5.
doi: 10.1016/j.csda.2003.11.021. |
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N. E. Ayat, et al., Automatic model selection for the optimization of SVM kernels,, Pattern Recognition, 38 (2005), 1733.
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doi: 10.1109/34.598228. |
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Q. N. Chen, et al., Hierarchical multi-view fisher discriminant analysis,, International Conference on Neural Information Processing, 5864 (2009), 289.
doi: 10.1007/978-3-642-10684-2_32. |
[5] |
T. Diethe, et al., Constructing nonlinear discriminants from multiple data views,, Machine Learning and Knowledge Discovery in Databases, 6321 (2010), 328.
doi: 10.1007/978-3-642-15880-3_27. |
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J. H. Friedman, et al., Regularized discriminant analysis,, Journal of the American Statistics Association, 84 (1989), 165.
doi: 10.1080/01621459.1989.10478752. |
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D. R. Hardoon, Sparse canonical correlation analysis,, Machine Learning, 83 (2011), 331.
doi: 10.1007/s10994-010-5222-7. |
[8] |
D. R. Hardoon, et al., Canonical correlation analysis: An overview with application to learning methods,, Neural Computation, 16 (2004), 2639.
doi: 10.1162/0899766042321814. |
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X. He, et al., Local Preserving Projections,, Advances in Neural Information Processing Systems, (2003). Google Scholar |
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X. He, et al., Face recognition using Laplicianfaces,, IEEE Transactions on Pattern Analysis and Machine Intelligence, 27 (2005), 328. Google Scholar |
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L. Hoegaerts, et al., Subset based least squares subspace regression in RKHS,, Neurocomputing, 63 (2005), 293.
doi: 10.1016/j.neucom.2004.04.013. |
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Y. J. Huang, et al., Protein NMR recall, precision, and F-measure scores (RPF scores): Structure quality assessment measures based on information retrieval statistics,, Journal of the American Chemical Society, 127 (2005), 1665. Google Scholar |
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H. Hotelling, Relations between two sets of variates,, Biometrika, 28 (1936), 312.
doi: 10.2307/2333955. |
[14] |
Z. Ji, et al., Rank canonical correlation analysis and its application in visual search reranking,, Signal Processing, 93 (2013), 2352.
doi: 10.1016/j.sigpro.2012.05.006. |
[15] |
X. Y. Jing, et al., Color image canonical correlation analysis for face feature extraction and recognition,, Signal Processing, 91 (2011), 2132.
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E. Kitani, et al., Um Tutorial sobre Analise de Componentes Principais para o Reconhecimento Automatico de Faces [R/OL],, , (2006). Google Scholar |
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P. L. Lai, et al., Kernel and nonlinear canonical correlation analysis,, International Journal of Neural Systems, 10 (2000), 365.
doi: 10.1016/S0129-0657(00)00034-X. |
[18] |
Y. Liu, et al., A survey of content-based image retrieval with high-level semantics,, Pattern Recognition, 40 (2007), 262.
doi: 10.1016/j.patcog.2006.04.045. |
[19] |
C. D. Manning, et al., Introduction to Information Retrieval,, Cambridge: Cambridge university press, (2008).
doi: 10.1017/CBO9780511809071. |
[20] |
T. Melzer, et al., Appearance models based on kernel canonical correlation analysis,, Pattern recognition, 36 (2003), 1961.
doi: 10.1016/S0031-3203(03)00058-X. |
[21] |
T. Melzer, et al., Appearance models based on kernel canonical correlation analysis,, Pattern Recognition, 36 (2003), 1961.
doi: 10.1016/S0031-3203(03)00058-X. |
[22] |
A. A. Nielsen, et al., Multiset canonical correlations analysis and multispectral truly multitemporal remote sensing data,, IEEE Transactions on Image Processing, 11 (2002), 293.
doi: 10.1109/83.988962. |
[23] |
S. Roweis, et al., Nonlinear dimensionality reduction by local linear embedding,, Science, 290 (2000), 2323.
doi: 10.1126/science.290.5500.2323. |
[24] |
M. Ortega, et al., Supporting ranked boolean similarity queries in MARS,, IEEE Transaction on Knowledge and Data Engineering, 10 (1998), 905.
doi: 10.1109/69.738357. |
[25] |
N. Otopal, et al., Restricted kernel canonical correlation analysis,, Linear Algebra and its Applications, 437 (2012), 1.
doi: 10.1016/j.laa.2012.02.014. |
[26] |
O. A. B. Penatti, et al., Comparative study of global color and texture descriptors for web image retrieval,, Journal of Visual Communication and Image Representation, 23 (2012), 359.
doi: 10.1016/j.jvcir.2011.11.002. |
[27] |
Y. Peng, et al., Semi-supervised kernel canonical correlation analysis,, Journal of Software, 19 (2008), 2822. Google Scholar |
[28] |
Y. Peng, et al., A new canonical correlation analysis algorithm with local discrimination,, Neural Processing Letters, 31 (2010), 1.
doi: 10.1007/s11063-009-9123-3. |
[29] |
R. Pless, et al., A Survey of Manifold Learning,, PIPSJ Transactions on Computer Vision and Applications, 1 (2009), 83. Google Scholar |
[30] |
F. S. Samaria, et al., Parameterisation of a stochastic model for human face identification,, In Second IEEE Workshop on Applications of Computer Vision, (1994), 138.
doi: 10.1109/ACV.1994.341300. |
[31] |
A. Sharma, et al., Generalized Multiview Analysis: A discriminative latent space,, IEEE Conference on Computer Vision and Pattern Recognition, (2012), 2160.
doi: 10.1109/CVPR.2012.6247923. |
[32] |
L. Sun, et al., Canonical correlation analysis for multilabel classification: A least-squares formulation, extensions, and analysis,, IEEE Transactions on Pattern Analysis and Machine Intelligence, 33 (2011), 194. Google Scholar |
[33] |
Q. Sun, et al., A new method of feature fusion and its application in image recognition,, Pattern Recognition, 38 (2005), 2437.
doi: 10.1016/j.patcog.2004.12.013. |
[34] |
T. K. Sun, et al., A novel method of combined feature extraction for recognition,, IEEE Conference on Data Mining, (2008), 1043.
doi: 10.1109/ICDM.2008.28. |
[35] |
T. K. Sun, et al., Locality preserving CCA with applications to data visualization and pose estimation,, Image and Vision Computing, 25 (2007), 531.
doi: 10.1016/j.imavis.2006.04.014. |
[36] |
M. Turk, et al., Eigenfaces for recognition,, Journal of Cognitive Neuroscience, 3 (1991), 71.
doi: 10.1162/jocn.1991.3.1.71. |
[37] |
N. Vlassis, et al., Supervised linear feature extraction for mobile robot localization,, Proceedings of the IEEE international conference on robotics and automation, 3 (2000), 2979.
doi: 10.1109/ROBOT.2000.846480. |
[38] |
Y. H. Yan, et al., A novel multiset integrated canonical correlation analysis framework and its application in feature fusion,, Pattern Recognition, 44 (2011), 1031.
doi: 10.1016/j.patcog.2010.11.004. |
[39] |
X. Zhu, et al., Dimensionality reduction by mixed kernel canonical correlation analysis,, Pattern Recognition, 45 (2012), 3003.
doi: 10.1016/j.patcog.2012.02.007. |
[40] |
J. Yang, et al., Feature fusion: Parallel strategy vs. serial strategy,, Pattern Recognition, 36 (2003), 1369.
doi: 10.1016/S0031-3203(02)00262-5. |
[41] |
W. W. Yu, et al., Face recognition using discriminant locality preserving projections,, Image and Vision computing, 24 (2006), 239.
doi: 10.1016/j.imavis.2005.11.006. |
[42] |
Y. B. Yuan, Canonical duality solution for alternating support vector machine,, Journal of Industrial and Management Optimization, 8 (2012), 611.
doi: 10.3934/jimo.2012.8.611. |
[43] |
X. Zhang, et al., Discriminative locality preserving canonical correlation analysis,, Pattern Recognition, 321 (2012), 341.
doi: 10.1007/978-3-642-33506-8_43. |
[44] |
UCI, UCI Repository of machine learning databases,, , (). Google Scholar |
show all references
References:
[1] |
B. Abraham, et al., Dimensionality reduction approach to multivariate prediction,, Comput Stat Data Anal, 48 (2005), 5.
doi: 10.1016/j.csda.2003.11.021. |
[2] |
N. E. Ayat, et al., Automatic model selection for the optimization of SVM kernels,, Pattern Recognition, 38 (2005), 1733.
doi: 10.1016/j.patcog.2005.03.011. |
[3] |
P. N. Belhumeur, et al., Eigenfaces vs. fisherfaces: Recognition using class specific linear projection,, IEEE Transactions on Pattern Analysis and Machine Intelligence, 19 (1997), 711.
doi: 10.1109/34.598228. |
[4] |
Q. N. Chen, et al., Hierarchical multi-view fisher discriminant analysis,, International Conference on Neural Information Processing, 5864 (2009), 289.
doi: 10.1007/978-3-642-10684-2_32. |
[5] |
T. Diethe, et al., Constructing nonlinear discriminants from multiple data views,, Machine Learning and Knowledge Discovery in Databases, 6321 (2010), 328.
doi: 10.1007/978-3-642-15880-3_27. |
[6] |
J. H. Friedman, et al., Regularized discriminant analysis,, Journal of the American Statistics Association, 84 (1989), 165.
doi: 10.1080/01621459.1989.10478752. |
[7] |
D. R. Hardoon, Sparse canonical correlation analysis,, Machine Learning, 83 (2011), 331.
doi: 10.1007/s10994-010-5222-7. |
[8] |
D. R. Hardoon, et al., Canonical correlation analysis: An overview with application to learning methods,, Neural Computation, 16 (2004), 2639.
doi: 10.1162/0899766042321814. |
[9] |
X. He, et al., Local Preserving Projections,, Advances in Neural Information Processing Systems, (2003). Google Scholar |
[10] |
X. He, et al., Face recognition using Laplicianfaces,, IEEE Transactions on Pattern Analysis and Machine Intelligence, 27 (2005), 328. Google Scholar |
[11] |
L. Hoegaerts, et al., Subset based least squares subspace regression in RKHS,, Neurocomputing, 63 (2005), 293.
doi: 10.1016/j.neucom.2004.04.013. |
[12] |
Y. J. Huang, et al., Protein NMR recall, precision, and F-measure scores (RPF scores): Structure quality assessment measures based on information retrieval statistics,, Journal of the American Chemical Society, 127 (2005), 1665. Google Scholar |
[13] |
H. Hotelling, Relations between two sets of variates,, Biometrika, 28 (1936), 312.
doi: 10.2307/2333955. |
[14] |
Z. Ji, et al., Rank canonical correlation analysis and its application in visual search reranking,, Signal Processing, 93 (2013), 2352.
doi: 10.1016/j.sigpro.2012.05.006. |
[15] |
X. Y. Jing, et al., Color image canonical correlation analysis for face feature extraction and recognition,, Signal Processing, 91 (2011), 2132.
doi: 10.1016/j.sigpro.2011.02.016. |
[16] |
E. Kitani, et al., Um Tutorial sobre Analise de Componentes Principais para o Reconhecimento Automatico de Faces [R/OL],, , (2006). Google Scholar |
[17] |
P. L. Lai, et al., Kernel and nonlinear canonical correlation analysis,, International Journal of Neural Systems, 10 (2000), 365.
doi: 10.1016/S0129-0657(00)00034-X. |
[18] |
Y. Liu, et al., A survey of content-based image retrieval with high-level semantics,, Pattern Recognition, 40 (2007), 262.
doi: 10.1016/j.patcog.2006.04.045. |
[19] |
C. D. Manning, et al., Introduction to Information Retrieval,, Cambridge: Cambridge university press, (2008).
doi: 10.1017/CBO9780511809071. |
[20] |
T. Melzer, et al., Appearance models based on kernel canonical correlation analysis,, Pattern recognition, 36 (2003), 1961.
doi: 10.1016/S0031-3203(03)00058-X. |
[21] |
T. Melzer, et al., Appearance models based on kernel canonical correlation analysis,, Pattern Recognition, 36 (2003), 1961.
doi: 10.1016/S0031-3203(03)00058-X. |
[22] |
A. A. Nielsen, et al., Multiset canonical correlations analysis and multispectral truly multitemporal remote sensing data,, IEEE Transactions on Image Processing, 11 (2002), 293.
doi: 10.1109/83.988962. |
[23] |
S. Roweis, et al., Nonlinear dimensionality reduction by local linear embedding,, Science, 290 (2000), 2323.
doi: 10.1126/science.290.5500.2323. |
[24] |
M. Ortega, et al., Supporting ranked boolean similarity queries in MARS,, IEEE Transaction on Knowledge and Data Engineering, 10 (1998), 905.
doi: 10.1109/69.738357. |
[25] |
N. Otopal, et al., Restricted kernel canonical correlation analysis,, Linear Algebra and its Applications, 437 (2012), 1.
doi: 10.1016/j.laa.2012.02.014. |
[26] |
O. A. B. Penatti, et al., Comparative study of global color and texture descriptors for web image retrieval,, Journal of Visual Communication and Image Representation, 23 (2012), 359.
doi: 10.1016/j.jvcir.2011.11.002. |
[27] |
Y. Peng, et al., Semi-supervised kernel canonical correlation analysis,, Journal of Software, 19 (2008), 2822. Google Scholar |
[28] |
Y. Peng, et al., A new canonical correlation analysis algorithm with local discrimination,, Neural Processing Letters, 31 (2010), 1.
doi: 10.1007/s11063-009-9123-3. |
[29] |
R. Pless, et al., A Survey of Manifold Learning,, PIPSJ Transactions on Computer Vision and Applications, 1 (2009), 83. Google Scholar |
[30] |
F. S. Samaria, et al., Parameterisation of a stochastic model for human face identification,, In Second IEEE Workshop on Applications of Computer Vision, (1994), 138.
doi: 10.1109/ACV.1994.341300. |
[31] |
A. Sharma, et al., Generalized Multiview Analysis: A discriminative latent space,, IEEE Conference on Computer Vision and Pattern Recognition, (2012), 2160.
doi: 10.1109/CVPR.2012.6247923. |
[32] |
L. Sun, et al., Canonical correlation analysis for multilabel classification: A least-squares formulation, extensions, and analysis,, IEEE Transactions on Pattern Analysis and Machine Intelligence, 33 (2011), 194. Google Scholar |
[33] |
Q. Sun, et al., A new method of feature fusion and its application in image recognition,, Pattern Recognition, 38 (2005), 2437.
doi: 10.1016/j.patcog.2004.12.013. |
[34] |
T. K. Sun, et al., A novel method of combined feature extraction for recognition,, IEEE Conference on Data Mining, (2008), 1043.
doi: 10.1109/ICDM.2008.28. |
[35] |
T. K. Sun, et al., Locality preserving CCA with applications to data visualization and pose estimation,, Image and Vision Computing, 25 (2007), 531.
doi: 10.1016/j.imavis.2006.04.014. |
[36] |
M. Turk, et al., Eigenfaces for recognition,, Journal of Cognitive Neuroscience, 3 (1991), 71.
doi: 10.1162/jocn.1991.3.1.71. |
[37] |
N. Vlassis, et al., Supervised linear feature extraction for mobile robot localization,, Proceedings of the IEEE international conference on robotics and automation, 3 (2000), 2979.
doi: 10.1109/ROBOT.2000.846480. |
[38] |
Y. H. Yan, et al., A novel multiset integrated canonical correlation analysis framework and its application in feature fusion,, Pattern Recognition, 44 (2011), 1031.
doi: 10.1016/j.patcog.2010.11.004. |
[39] |
X. Zhu, et al., Dimensionality reduction by mixed kernel canonical correlation analysis,, Pattern Recognition, 45 (2012), 3003.
doi: 10.1016/j.patcog.2012.02.007. |
[40] |
J. Yang, et al., Feature fusion: Parallel strategy vs. serial strategy,, Pattern Recognition, 36 (2003), 1369.
doi: 10.1016/S0031-3203(02)00262-5. |
[41] |
W. W. Yu, et al., Face recognition using discriminant locality preserving projections,, Image and Vision computing, 24 (2006), 239.
doi: 10.1016/j.imavis.2005.11.006. |
[42] |
Y. B. Yuan, Canonical duality solution for alternating support vector machine,, Journal of Industrial and Management Optimization, 8 (2012), 611.
doi: 10.3934/jimo.2012.8.611. |
[43] |
X. Zhang, et al., Discriminative locality preserving canonical correlation analysis,, Pattern Recognition, 321 (2012), 341.
doi: 10.1007/978-3-642-33506-8_43. |
[44] |
UCI, UCI Repository of machine learning databases,, , (). Google Scholar |
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