Transformation invariance in pattern recognition?tangent distance and tangent propagation, lecture notes in computer science, pp.239-274, 1998. ,
LDA/SVM driven nearest neighbor classification, IEEE Transactions on Neural Networks, vol.14, issue.4, pp.940-942, 2003. ,
DOI : 10.1109/TNN.2003.813835
Discriminant adaptive nearest neighbor classification, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.18, issue.6, pp.607-616, 1996. ,
DOI : 10.1109/34.506411
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.35.537
K-local hyperplane and convex distance nearest neighbor algorithms, Adv Neural Inf Process Syst, vol.14, pp.985-992, 2001. ,
Efficient Local Flexible Nearest Neighbor Classification, Proceedings of the 2nd SIAM International Conference on Data Mining, 2002. ,
DOI : 10.1137/1.9781611972726.21
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.10.6434
SVM-KNN: Discriminative Nearest Neighbor Classification for Visual Category Recognition, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Volume 2 (CVPR'06), pp.2126-2136, 2006. ,
DOI : 10.1109/CVPR.2006.301
Adaptive Discriminant and Quasiconformal Kernel Nearest Neighbor Classification, IEEE Trans Pattern Anal Mach Intell, vol.28, pp.656-661, 2004. ,
DOI : 10.1007/10984697_8
Locally adaptive metric nearest-neighbor classification, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, issue.9, pp.1281-1285, 2002. ,
DOI : 10.1109/TPAMI.2002.1033219
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.33.4419
Protein fold recognition with K-local hyperplane distance nearest neighbor algorithm, Proceedings of the 2nd European Workshop on data Mining and Text Mining in Bioinformatics, pp.51-57, 2004. ,
Modeling the manifolds of images of handwritten digits, IEEE Transactions on Neural Networks, vol.8, issue.1, pp.65-74, 1997. ,
DOI : 10.1109/72.554192
Nonlinear Dimensionality Reduction by Locally Linear Embedding, Science, vol.290, issue.5500, pp.2323-2326, 2000. ,
DOI : 10.1126/science.290.5500.2323
Learning nonlinear image manifolds by global alignment of local linear models, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.28, issue.8, pp.1236-1250, 2006. ,
DOI : 10.1109/TPAMI.2006.166
URL : https://hal.archives-ouvertes.fr/inria-00321131
Discriminative common vectors for face recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.27, issue.1, pp.4-13, 2005. ,
DOI : 10.1109/TPAMI.2005.9
Locally linear discriminant analysis for multimodally distributed classes for face recognition with a single model image, IEEE Trans PAMI, vol.27, pp.318-327, 2005. ,
Joint manifold distance: a new approach to appearance based clustering, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings., 2003. ,
DOI : 10.1109/CVPR.2003.1211334
Local Features and Kernels for Classification of Texture and Object Categories: A Comprehensive Study, Proceedings of the Computer Vision and Pattern Recognition Workshop, 2006. ,
DOI : 10.1007/s11263-006-9794-4
URL : https://hal.archives-ouvertes.fr/inria-00548574
A Global Geometric Framework for Nonlinear Dimensionality Reduction, Science, vol.290, issue.5500, pp.2319-2323, 2000. ,
DOI : 10.1126/science.290.5500.2319
The common vector approach and its relation to principal component analysis, IEEE Transactions on Speech and Audio Processing, vol.9, issue.6, pp.655-662, 2001. ,
DOI : 10.1109/89.943343
Convex optimization pp, pp.399-401, 2004. ,
Nonlinear Component Analysis as a Kernel Eigenvalue Problem, Neural Computation, vol.20, issue.5, pp.1299-1319, 1998. ,
DOI : 10.1007/BF02281970
Discriminative Common Vector Method With Kernels, IEEE Transactions on Neural Networks, vol.17, issue.6, pp.1550-1565, 2006. ,
DOI : 10.1109/TNN.2006.881485
The method of alternating projections and the method of subspace corrections in hilbert space, Journal of the American Mathematical Society, vol.15, issue.03, pp.573-597, 2002. ,
DOI : 10.1090/S0894-0347-02-00398-3
Learning generative visual models from few training examples: An incremental Bayesian approach tested on 101 object categories, Proceedings of the IEEE CVPR Workshop of Generative Model Based Vision, 2004. ,
DOI : 10.1016/j.cviu.2005.09.012
Experiments with an extended tangent distance, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000, pp.38-42, 2000. ,
DOI : 10.1109/ICPR.2000.906014
Visual categorization with bags of keypoints, Proceedings of the ECCV Workshop on Statistical Learning for Computer Vision, 2004. ,
A sparse texture representation using local affine regions, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.27, issue.8, pp.1265-1278, 2005. ,
DOI : 10.1109/TPAMI.2005.151
URL : https://hal.archives-ouvertes.fr/inria-00548530
Spectral grouping using the nystrom method, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.26, issue.2, pp.1-12, 2004. ,
DOI : 10.1109/TPAMI.2004.1262185
Think globally, fit locally: unsupervised learning of low dimensional manifolds, J Mach Learn Res, vol.4, pp.119-155, 2003. ,
Maximum likelihood estimation of intrinsic dimension, Advances in neural information processing system, pp.17-777, 2005. ,
Estimating the intrinsic dimension of data with a fractal-based method, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, issue.10, pp.1404-1407, 2002. ,
DOI : 10.1109/TPAMI.2002.1039212
An Algorithm for Finding Intrinsic Dimensionality of Data, IEEE Transactions on Computers, vol.20, issue.2, pp.176-183, 1971. ,
DOI : 10.1109/T-C.1971.223208