N. Vladimir and . Vapnik, The nature of statistical learning theory, 1995.

T. J. Hastie, R. J. Tibshirani, and J. H. Friedman, The elements of statistical learning : data mining, inference, and prediction. Springer series in statistics, 2009.

A. Gibbons, Algorithmic Graph Theory, 1985.

D. M. Greig, B. T. Porteous, and A. H. Seheult, Exact Maximum A Posteriori Estimation for Binary Images, 1989.

L. Torresani, V. Kolmogorov, and C. Rother, Feature Correspondence Via Graph Matching: Models and Global Optimization, Proceedings of the 10th European Conference on Computer Vision: Part II, ECCV '08, pp.596-609, 2008.
DOI : 10.1007/978-3-540-88688-4_44

B. Ng, G. Hamarneh, and R. Abugharbieh, Modeling Brain Activation in fMRI Using Group MRF, IEEE Transactions on Medical Imaging, vol.31, issue.5, pp.311113-1123, 2012.
DOI : 10.1109/TMI.2012.2185943

J. Rao, R. Abugharbieh, and G. Hamarneh, Adaptive Regularization for Image Segmentation Using Local Image Curvature Cues, Kostas Daniilidis, Petros Maragos, and Nikos Paragios European Conference in Computer Vision, pp.651-665, 2010.
DOI : 10.1007/978-3-642-15561-1_47

A. A. Canutescu, A. A. Shelenkov, R. L. Dunbrack, and J. , A graph-theory algorithm for rapid protein side-chain prediction, Protein Science, vol.311, issue.9, pp.2001-2014, 2003.
DOI : 10.1110/ps.03154503

A. Wagner and D. A. Fell, The small world inside large metabolic networks, Proceedings of the Royal Society of London. Series B: Biological Sciences, pp.1803-1810, 1478.
DOI : 10.1098/rspb.2001.1711

I. P. Goldstein, The genetic graph: a representation for the evolution of procedural knowledge, International Journal of Man-Machine Studies, vol.11, issue.1, pp.51-77, 1979.
DOI : 10.1016/S0020-7373(79)80005-X

T. Hapke, Chemoinformatics. a textbook, pp.171-173, 2005.

L. Kaufman and A. Neumaier, PET regularization by envelope guided conjugate gradients, IEEE Transactions on Medical Imaging, vol.15, issue.3, pp.385-389, 1996.
DOI : 10.1109/42.500147

M. W. Woolrich, T. E. Behrens, C. F. Beckmann, and S. M. Smith, Mixture models with adaptive spatial regularization for segmentation with an application to FMRI data, IEEE Transactions on Medical Imaging, vol.24, issue.1, pp.1-11, 2005.
DOI : 10.1109/TMI.2004.836545

A. N. Tikhonov, On the stability of inverse problems, Doklady Akademii nauk SSSR, vol.39, issue.5, pp.195-198, 1943.

R. Tibshirani, Regression shrinkage and selection via the lasso, Journal of the Royal Statistical Society Series B, vol.58, pp.267-288, 1996.

H. Zou and T. Hastie, Regularization and variable selection via the elastic net, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.5, issue.2, pp.301-320, 2005.
DOI : 10.1073/pnas.201162998

. Blaschko, MRI Analysis with Sparse Weisfeiler-Lehman Graph Statistics, 4th International Workhop on Machine Learning in Medical Imaging, 2013.

. Blaschko, FMRI analysis of cocaine addiction using k-support sparsity, International Symposium on Biomedical Imaging, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00784386

K. Gkirtzou, J. Deux, G. Bassez, A. Sotiras, A. Rahmouni et al., Sparse Classification with MRI Based Markers for Neuromuscular Disease Categorization, 4th International Workhop on Machine Learning in Medical Imaging, 2013.
DOI : 10.1007/978-3-319-02267-3_5
URL : https://hal.archives-ouvertes.fr/hal-00845126

R. Francis and . Bach, Graph kernels between point clouds, Proceedings of the 25th international conference on Machine learning, International Conference on Machine Learning '08, pp.25-32, 2008.

R. Michael, D. S. Garey, and . Johnson, Computers and Intractability; A Guide to the Theory of NP-Completeness, 1979.

X. Gao, B. Xiao, D. Tao, and X. Li, A survey of graph edit distance, Pattern Analysis and Applications, vol.72, issue.3, pp.113-129, 2010.
DOI : 10.1007/s10044-008-0141-y

D. Haussler, Convolution kernels on discrete structures, 1999.

T. Gärtner, P. Flach, and S. Wrobel, On Graph Kernels: Hardness Results and Efficient Alternatives, Learning Theory and Kernel Machines, pp.129-143, 2003.
DOI : 10.1007/978-3-540-45167-9_11

. Borgwardt, Graph kernels, Journal of Machine Learning Research, vol.11, pp.1201-1242, 2010.

N. Shervashidze, P. Schweitzer, E. J. Van-leeuwen, K. Mehlhorn, and K. M. Borgwardt, Weisfeiler-lehman graph kernels, Journal of Machine Learning Research, vol.12, pp.2539-2561, 2011.

P. Mahé, N. Ueda, T. Akutsu, J. Perret, and J. Vert, Extensions of marginalized graph kernels, Twenty-first international conference on Machine learning , ICML '04, pp.552-559, 2004.
DOI : 10.1145/1015330.1015446

M. Karsten, H. Borgwardt, and . Kriegel, Shortest-path kernels on graphs, Proceedings of the Fifth IEEE International Conference on Data Mining, ICDM '05, pp.74-81, 2005.

L. Ralaivola, S. J. Swamidass, H. Saigo, and P. Baldi, Graph kernels for chemical informatics, Neural Networks, vol.18, issue.8, pp.1093-1110, 2005.
DOI : 10.1016/j.neunet.2005.07.009

T. Horváth, T. Gärtner, and S. Wrobel, Cyclic pattern kernels for predictive graph mining, Proceedings of the 2004 ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '04, pp.158-167, 2004.
DOI : 10.1145/1014052.1014072

N. Shervashidze, S. V. Vishwanathan, T. Petri, K. Mehlhorn, and K. Borgwardt, Efficient graphlet kernels for large graph comparison, Proceedings of the International Workshop on Artificial Intelligence and Statistics, 2009.

F. Costa and K. De-grave, Fast neighborhood subgraph pairwise distance kernel, Proceedings of the 26th International Conference on Machine Learning, pp.255-262, 2010.

J. Ramon and T. Gaertner, Expressivity versus efficiency of graph kernels, Proceedings of the First International Workshop on Mining Graphs, Trees and Sequences, pp.65-74, 2003.

P. Mahé and J. Vert, Graph kernels based on tree patterns for molecules, Machine Learning, pp.3-35, 2009.
DOI : 10.1007/s10994-008-5086-2

K. M. Borgwardt, Graph Kernels, 2007.

H. L. Morgan, The Generation of a Unique Machine Description for Chemical Structures-A Technique Developed at Chemical Abstracts Service., Journal of Chemical Documentation, vol.5, issue.2, pp.107-113, 1965.
DOI : 10.1021/c160017a018

Z. Harchaoui and F. Bach, Image Classification with Segmentation Graph Kernels, 2007 IEEE Conference on Computer Vision and Pattern Recognition, 2007.
DOI : 10.1109/CVPR.2007.383049

B. Weisfeiler and A. A. Lehman, A reduction of a graph to a canonical form and an algebra arising during this reduction, Nauchno-Technicheskaya Informatsia, pp.12-16, 1968.

J. Cai, M. Furer, and N. Immerman, An optimal lower bound on the number of variables for graph identification, Proceedings of the 30th Annual Symposium on Foundations of Computer Science, SFCS '89, pp.612-617, 1989.

Y. Ko, A study of term weighting schemes using class information for text classification, Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval, SIGIR '12, pp.1029-1030, 2012.
DOI : 10.1145/2348283.2348453

G. Qiu, Indexing chromatic and achromatic patterns for content-based. Pattern Recognition , year =, pp.1675-1686, 2002.

L. Fei-fei, A Bayesian Hierarchical Model for Learning Natural Scene Categories, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), pp.524-531, 2005.
DOI : 10.1109/CVPR.2005.16

K. Grauman and T. Darrell, The pyramid match kernel: Efficient learning with sets of features, Journal of Machine Learning Research, vol.8, pp.725-760, 2007.

K. Grauman and T. Darrell, Approximate Correspondences in High Dimensions, Advances in Neural Information Processing Systems 19 (NIPS), 2007.

J. H. Ward, Hierarchical Grouping to Optimize an Objective Function, Journal of the American Statistical Association, vol.58, issue.301, pp.236-244, 1963.
DOI : 10.1007/BF02289263

A. Odone, A. Barla, and . Verri, Building kernels from binary strings for image matching, IEEE Transactions on Image Processing, vol.14, issue.2, pp.169-180, 2005.
DOI : 10.1109/TIP.2004.840701

R. G. Gert, . Lanckriet, N. De-bie, M. I. Cristianini, W. S. Jordan et al., A statistical framework for genomic data fusion, Bioinformatics, vol.20, issue.16, pp.2626-2635, 2004.

A. Zien and C. Ong, Multiclass multiple kernel learning, Proceedings of the 24th international conference on Machine learning, ICML '07, pp.1191-1198, 2007.
DOI : 10.1145/1273496.1273646
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.165.9876

S. Sonnenburg, G. Rätsch, C. Schäfer, and B. Schölkopf, Large scale multiple kernel learning, Journal of Machice Learning Research, vol.7, pp.1531-1565, 2006.

A. Vedaldi, V. Gulshan, M. Varma, and A. Zisserman, Multiple kernels for object detection, 2009 IEEE 12th International Conference on Computer Vision, pp.606-613, 2009.
DOI : 10.1109/ICCV.2009.5459183
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.163.5316

A. Tikhonov, Solution of incorrectly formulated problems and the regularization method, Soviet Math. Doklady, pp.1035-1038, 1963.

A. Bartels, S. Zeki, and N. K. Logothetis, Natural Vision Reveals Regional Specialization to Local Motion and to Contrast-Invariant, Global Flow in the Human Brain, Cerebral Cortex, vol.18, issue.3, 2007.
DOI : 10.1093/cercor/bhm107

A. Bartels and S. Zeki, Functional brain mapping during free viewing of natural scenes, Human Brain Mapping, vol.11, issue.2, pp.75-85, 2004.
DOI : 10.1002/hbm.10153

S. Song, Z. Zhan, Z. Long, J. Zhang, and L. Yao, Comparative Study of SVM Methods Combined with Voxel Selection for Object Category Classification on fMRI Data, PLoS ONE, vol.12, issue.4, p.17191, 2011.
DOI : 10.1371/journal.pone.0017191.s008

S. Laconte, S. Strother, and V. Cherkassky, Support vector machines for temporal classification of block design fMRI data, NeuroImage, vol.26, issue.2, p.317, 2005.
DOI : 10.1016/j.neuroimage.2005.01.048

A. Bartels and S. Zeki, The chronoarchitecture of the human brain???natural viewing conditions reveal a time-based anatomy of the brain, NeuroImage, vol.22, issue.1, pp.419-433, 2004.
DOI : 10.1016/j.neuroimage.2004.01.007

A. Bartels and S. Zeki, Brain dynamics during natural viewing conditions???A new guide for mapping connectivity in vivo, NeuroImage, vol.24, issue.2, pp.339-349, 2005.
DOI : 10.1016/j.neuroimage.2004.08.044

B. D. Bibliography, J. Hardoon, M. Mourão-miranda, J. Brammer, and . Shawe-taylor, Unsupervised analysis of fMRI data using kernel canonical correlation, NeuroImage, vol.37, issue.4, pp.1250-1259, 2007.

M. B. Blaschko, J. A. Shelton, and A. Bartels, Augmenting feature-driven fMRI analyses

M. B. Blaschko, J. A. Shelton, A. Bartels, C. H. Lampert, and A. Gretton, Semi-supervised kernel canonical correlation analysis with application to human fMRI, Pattern Recognition Letters, vol.32, issue.11, pp.1572-1583, 2011.
DOI : 10.1016/j.patrec.2011.02.011

O. Demirci, V. P. Clark, and V. D. Calhoun, A projection pursuit algorithm to classify individuals using fMRI data: Application to schizophrenia, NeuroImage, vol.39, issue.4, 2008.
DOI : 10.1016/j.neuroimage.2007.10.012

X. Wang, R. Hutchinson, and T. M. Mitchell, Training fMRI classifiers to discriminate cognitive states across multiple subjects, Advances in Neural Information Processing Systems, 2003.

T. M. Mitchell, R. Hutchinson, R. S. Niculescu, F. Pereira, X. Wang et al., Learning to Decode Cognitive States from Brain Images, Machine Learning, pp.145-175, 2004.
DOI : 10.1023/B:MACH.0000035475.85309.1b
URL : http://repository.cmu.edu/cgi/viewcontent.cgi?article=2091&context=psychology

M. Amir, E. Tahmasebi, T. Artiges, G. J. Banaschewski, R. Barker et al., Tomá? Paus, and The IMAGEN Consortium. Creating probabilistic maps of the face network in the adolescent brain: A multicentre functional mri study, Human Brain Mapping, vol.33, issue.4, pp.938-957, 2012.

D. D. Cox and R. Savoy, Functional magnetic resonance imaging (fMRI) ???brain reading???: detecting and classifying distributed patterns of fMRI activity in human visual cortex, NeuroImage, vol.19, issue.2, pp.261-270, 2003.
DOI : 10.1016/S1053-8119(03)00049-1

N. Kriegeskorte, K. W. Simmons, P. S. Bellgowan, and C. I. Baker, Circular analysis in systems neuroscience: the dangers of double dipping, Nature Neuroscience, vol.12, issue.5, 2009.
DOI : 10.1016/j.neuroimage.2004.07.022

M. K. Carroll, G. A. Cecchi, I. Rish, R. Garg, and A. R. Rao, Prediction and interpretation of distributed neural activity with sparse models, NeuroImage, vol.44, issue.1, pp.112-122, 2009.
DOI : 10.1016/j.neuroimage.2008.08.020

. Greicius, Network analysis of intrinsic functional brain connectivity in alzheimer's disease, PLoS Computational Biology, vol.4, issue.6, 2008.

Y. Liu, M. Liang, Y. Zhou, Y. He, Y. Hao et al., Disrupted small-world networks in schizophrenia, Brain, vol.131, issue.4, 2008.
DOI : 10.1093/brain/awn018

K. Supekar, M. Musen, and V. Menon, Development of large-scale functional brain networks in children, PLoS biology, vol.7, issue.7, 2009.

F. Mokhtari and G. , Decoding brain states using backward edge elimination and graph kernels in fMRI connectivity networks, Journal of Neuroscience Methods, vol.212, issue.2, pp.259-268, 2013.
DOI : 10.1016/j.jneumeth.2012.10.012

R. Z. Goldstein, N. Alia-klein, D. Tomasi, J. H. Carrillo, T. Maloney et al., Anterior cingulate cortex hypoactivations to an emotionally salient task in cocaine addiction, Proceedings of the National Academy of Sciences, p.9453, 2009.
DOI : 10.1073/pnas.0900491106

J. Honorio, D. Tomasi, R. Goldstein, H. C. Leung, and D. Samaras, Can a Single Brain Region Predict a Disorder?, IEEE Transactions on Medical Imaging, vol.31, issue.11, 2012.
DOI : 10.1109/TMI.2012.2206047

K. J. Friston, J. Ashburner, S. J. Kiebel, T. E. Nichols, and W. D. Penny, Statistical Parametric Mapping: The Analysis of Functional Brain Images, 2007.

O. Sporns, Networks of the Brain, 2010.

C. Wee, W. Pew-thian-yap, K. Li, J. N. Denny, G. G. Browndyke et al., Enriched white matter connectivity networks for accurate identification of MCI patients, NeuroImage, vol.54, issue.3, pp.541812-1822, 2011.
DOI : 10.1016/j.neuroimage.2010.10.026

M. Elad, A. Tal, and S. Ar, Content Based Retrieval of VRML Objects ??? An Iterative and Interactive Approach, Proceedings of the sixth Eurographics workshop on Multimedia, pp.107-118, 2001.
DOI : 10.1007/978-3-7091-6103-6_12

M. Mahmoudi and G. Sapiro, Three-dimensional point cloud recognition via distributions of geometric distances, 2008.

. Bibliography, M. M. Bibliography-iasonas-kokkinos, R. Bronstein, A. M. Litman, and . Bronstein, Intrinsic shape context descriptors for deformable shapes, Computer Vision and Pattern Recognition, pp.159-166, 2012.

C. Ha-lee, A. Varshney, and D. W. Jacobs, Mesh saliency, ACM Transactions on Graphics, vol.24, issue.3, pp.659-666, 2005.
DOI : 10.1145/1073204.1073244

U. Castellani, M. Cristani, S. Fantoni, and V. Murino, Sparse points matching by combining 3D mesh saliency with statistical descriptors, Computer Graphics Forum, vol.27, issue.2, pp.643-652, 2008.
DOI : 10.1111/j.1467-8659.2008.01162.x

R. Ohbuchi and T. Furuya, Distance metric learning and feature combination for shape-based 3D model retrieval, Proceedings of the ACM workshop on 3D object retrieval, 3DOR '10, pp.63-68, 2010.
DOI : 10.1145/1877808.1877822

M. Hilaga, Y. Shinagawa, T. Kohmura, and T. L. Kunii, Topology matching for fully automatic similarity estimation of 3D shapes, Proceedings of the 28th annual conference on Computer graphics and interactive techniques , SIGGRAPH '01, pp.203-212, 2001.
DOI : 10.1145/383259.383282

H. Sundar, D. Silver, N. Gagvani, and S. Dickinson, Skeleton based shape matching and retrieval, 2003 Shape Modeling International., p.130, 2003.
DOI : 10.1109/SMI.2003.1199609
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.10.8114

M. Garland and P. S. Heckbert, Surface simplification using quadric error metrics, Proceedings of the 24th annual conference on Computer graphics and interactive techniques , SIGGRAPH '97, pp.209-216, 1997.
DOI : 10.1145/258734.258849
URL : http://cdserver.icemt.iastate.edu/cd/s97cp/contents/papers/garland/quadrics.pdf

M. Garland and P. S. Heckbert, Simplifying surfaces with color and texture using quadric error metrics, Proceedings Visualization '98 (Cat. No.98CB36276), pp.263-269, 1998.
DOI : 10.1109/VISUAL.1998.745312

S. Rusinkiewicz, Estimating curvatures and their derivatives on triangle meshes, Proceedings. 2nd International Symposium on 3D Data Processing, Visualization and Transmission, 2004. 3DPVT 2004., 2004.
DOI : 10.1109/TDPVT.2004.1335277

K. Q. Weinberger and L. K. Saul, Distance metric learning for large margin nearest neighbor classification, The Journal of Machine Learning Research, vol.10, pp.207-244, 2009.

A. Berger, How does it work?: Magnetic resonance imaging, BMJ, vol.324, issue.7328, p.35, 2002.
DOI : 10.1136/bmj.324.7328.35
URL : http://www.bmj.com/cgi/content/short/324/7328/35

A. Argyriou, R. Foygel, and N. Srebro, Sparse prediction with the k-support norm, Advances in Neural Information Processing Systems. 2012. Bibliography 135
URL : https://hal.archives-ouvertes.fr/hal-00858954

O. Chapelle, Training a Support Vector Machine in the Primal, Neural Computation, vol.6, issue.5, pp.1155-1178, 2007.
DOI : 10.1198/106186005X25619

C. Cortes and V. Vapnik, Support-vector networks, Machine Learning, pp.273-297, 1995.
DOI : 10.1007/BF00994018

J. Zhu, S. Rosset, T. Hastie, and R. Tibshirani, 1-norm support vector machines, Advances in Neural Information Processing Systems 16, 2004.

L. Wang, J. Zhu, and H. Zou, The doubly regularized support vector machine, Statistica Sinica, vol.16, issue.2, pp.589-616, 2006.

L. Peter, M. I. Bartlett, J. D. Jordan, and . Mcauliffe, Convexity, classification, and risk bounds, Journal of the American Statistical Association, vol.101, issue.473, pp.138-156, 2006.

A. Jacquelyn and . Shelton, Semi-supervised subspace learning and application to human functional magnetic brain resonance imaging data, 2010.

K. J. Friston, A. P. Holmes, K. J. Worsley, J. P. Poline, C. D. Frith et al., Statistical parametric maps in functional imaging: A general linear approach, Human Brain Mapping, vol.26, issue.4, pp.189-210, 1994.
DOI : 10.1002/hbm.460020402

J. Wang and N. D. Volkow, Oral methylphenidate normalizes cingulate activity in cocaine addiction during a salient cognitive task, Proceedings of the National Academy of Sciences, pp.16667-72, 2010.

C. S. Culbertson, J. Bramen, M. S. Cohen, E. D. London, R. E. Olmstead et al., Effect of Bupropion Treatment on Brain Activation Induced by Cigarette-Related Cues in Smokers, Archives of General Psychiatry, vol.68, issue.5, pp.68505-515, 2011.
DOI : 10.1001/archgenpsychiatry.2010.193

O. Brien and A. R. Childress, Dopamine transporter genotype modulation of neural responses to smoking cues: confirmation in a new cohort, Addiction Biology, vol.16, issue.2, pp.308-322, 2011.

D. Le-bihan, J. Mangin, C. Poupon, C. A. Clark, S. Pappata et al., Diffusion tensor imaging: Concepts and applications, Journal of Magnetic Resonance Imaging, vol.44, issue.4, pp.534-546, 2001.
DOI : 10.1002/jmri.1076
URL : https://hal.archives-ouvertes.fr/hal-00349820

J. Richard, . Gilbert, J. Vitaly, and . Napadow, Three-dimensional muscular architecture of the human tongue determined in vivo with diffusion tensor magnetic resonance imaging, Dysphagia, vol.20, issue.1, pp.1-7, 2005.

J. Qi, J. Nancy, . Olsen, R. Ronald, . Price et al., Diffusion-weighted imaging of inflammatory myopathies: Polymyositis and dermatomyositis, Journal of Magnetic Resonance Imaging, vol.48, issue.1, pp.212-217, 2008.
DOI : 10.1002/jmri.21209

M. Caan, K. Vermeer, . Van-vliet, . Cblm-majoie, . Bd-peters et al., Shaving diffusion tensor images in discriminant analysis: A study into schizophrenia, Medical Image Analysis, vol.10, issue.6, pp.841-849, 2006.
DOI : 10.1016/j.media.2006.07.006

P. Wang and R. Verma, On Classifying Disease-Induced Patterns in the Brain Using Diffusion Tensor Images, Medical Image Computing and Computer Assisted Intervention, pp.908-916, 2008.
DOI : 10.1007/978-3-540-85988-8_108

M. Ingalhalikar, D. Parker, L. Bloy, P. Timothy, R. Roberts et al., Diffusion based abnormality markers of pathology: Toward learned diagnostic prediction of ASD, NeuroImage, vol.57, issue.3, pp.918-927, 2011.
DOI : 10.1016/j.neuroimage.2011.05.023

N. Radhoù-ene, Diffusion Tensor Imaging of the Human Skeletal Muscle : Contributions and Applications, 2010.

P. Wang, R. Gur, and R. Verma, A novel framework for identifying dtibased brain patterns of schizophrenia, International Society for Magnetic Resonance in Medicine, pp.3-9, 2008.