Downloads

[1] S. Ba and J. Odobez, “Evaluation of multiple cue head pose estimation algorithms in natural environements,” in Multimedia and Expo IEEE International Conference on, July 2005, pp. 1330–1333. [2] B. Blankertz, G. Curio, and K. Mller, “Classifying single trial EEG: Towards brain computer interfacing,” NIPS, pp. 157–164, 2002. [3] M. Blondel, K. Seki, and K. Uehara, “Block coordinate descent algorithms for large-scale sparse multiclass classification,” Machine Learning, vol. 93, no. 1, pp. 31–52, 2013. [Online]. Available: http://dx.doi.org/10.1007/s10994-013-5367-2 [4] B. S. Caffo, C. M. Crainiceanu, G. Verduzco, S. Joel, S. H. Mostofsky, S. S. Bassett, and J. J. Pekar, “Two-stage decompositions for the analysis of functional connectivity for fMRI with application to alzheimer’s disease risk,” NeuroImage, vol. 51, no. 3, pp. 1140 – 1149, 2010. [Online]. Available: http://www.sciencedirect.com/science/ article/pii/S1053811910002685 [5] M. Donoho, D. L.and Elad, “Optimally sparse representation in general (non-orthogonal) dictionaries via l1 minimization,” in Proc. Natl Acad. Sci., 2003, pp. 2197–2202. [6] C. Gao and X. Wu, “Kernel support tensor regression,” Procedia Engineering, vol. 29, pp. 3986 – 3990, 2012. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S1877705812006169 [7] A. Genkin, D. D. Lewis, and D. Madigan, “Large-scale Bayesian logistic regression for text categorization,” Technometrics, vol. 49, pp. 291–304(14), 2007. [8] N. Gourier, D. Hall, and J. L. Crowley, “Estimating face orientation from robust detection of salient facial structures,” in In Proceedings of ICPR, International Workshop on Visual Observation of Deictic Gestures, 2004. [9] W. Guo, I. Kotsia, and I. Patras, “Tensor learning for regression,” Image Processing, IEEE Transactions on, vol. 21, no. 2, pp. 816–827, Feb 2012. [10] T. Kolda and B. Bader, “Tensor decompositions and applications,” SIAM Review, vol. 51, no. 3, pp. 455–500, 2009. [Online]. Available: http://epubs.siam.org/doi/abs/10.1137/07070111X [11] D. Kontos, V. Megalooikonomou, N. Ghubade, and C. Faloutsos, “Detecting discriminative functional MRI activation patterns using space filling curves,” in Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Sept 2003, pp. 963–966. [12] S. LaConte, S. Strother, V. Cherkassky, J. Anderson, and X. Hu, “Support vector machines for temporal classification of block design fMRI data,” NeuroImage, vol. 26, no. 2, pp. 317 – 329, 2005. [Online]. Available: http://www.sciencedirect.com/science/article/pii/ S1053811905000893 [13] X. Li, H. Zhou, and L. Li, “Tucker tensor regression and neuroimaging analysis,” http://arxiv.org/abs/1304.5637, 2013. [14] T. Mitchell, R. Hutchinson, R. Niculescu, F. Pereira, X. Wang, M. Just, and S. Newman, “Learning to decode cognitive states from brain images,” Machine Learning, vol. 57, no. 1-2, pp. 145– 175, 2004. [Online]. Available: http://dx.doi.org/10.1023/B%3AMACH. 0000035475.85309.1b [15] D. Needell, J. Tropp, and R. Vershynin, “Greedy signal recovery review,” in Proceedings of the 42nd Asilomar Conf. Signals, Systems and Computers, Pacific Grove. IEEE, 2008, pp. 1048–1050. [16] D. Needell and J. Tropp, “Cosamp: Iterative signal recovery from incomplete and inaccurate samples,” Commun. ACM, vol. 53, no. 12, pp. 93–100, Dec. 2010. [Online]. Available: http://doi.acm.org/10. 1145/1859204.1859229 [17] D.-S. Pham and S. Venkatesh, “Robust learning of discriminative projection for multicategory classification on the stiefel manifold,” in Computer Vision and Pattern Recognition (CVPR), IEEE Conference on, June 2008, pp. 1–7. [18] W. H. Press, S. A. Teukolsky, W. T. Vetterling, and B. P. Flannery, Numerical Recipes: The Art of Scientific Computing, 3rd ed. New York, USA: Cambridge University Press, 2007. [19] S. V. Shinkareva, H. C. Ombao, B. P. Sutton, A. Mohanty, and G. A. Miller, “Classification of functional brain images with a spatio-temporal dissimilarity map,” NeuroImage, vol. 33, no. 1, pp. 63 – 71, 2006. [Online]. Available: http://www.sciencedirect.com/science/ article/pii/S1053811906007105 [20] A. J. Smola and B. Sch¨olkopf, “A tutorial on support vector regression,” Statistics and Computing, vol. 14, no. 3, pp. 199–222, 2004. [Online]. Available: http://dx.doi.org/10.1023/B:STCO.0000035301.49549.88 [21] X. Tan, Y. Zhang, S. Tang, J. Shao, F. Wu, and Y. Zhuang, “Logistic tensor regression for classification,” in Intelligent Science and Intelligent Data Engineering, ser. Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2013, vol. 7751, pp. 573–581. [Online]. Available: http://dx.doi.org/10.1007/978-3-642-36669-7 70 [22] J. Tropp, “Greed is good: algorithmic results for sparse approximation,” Information Theory, IEEE Transactions on, vol. 50, no. 10, pp. 2231– 2242, 2004. [23] J. Wang, K.-H. Lee, and K.-S. Leung, “L1-norm regularization based nonlinear integrals,” ser. Lecture Notes in Computer Science, vol. 5551. Springer, 2009, pp. 201–208. [Online]. Available: http://dblp.uni-trier.de/db/conf/isnn/isnn2009-1.html#WangLL09 [24] Q. Zhao, C. F. Caiafa, D. P. Mandic, L. Zhang, T. Ball, A. Schulzebonhage, and A. S. Cichocki, “Multilinear subspace regression: An orthogonal tensor decomposition approach,” in Advances in Neural Information Processing Systems 24, J. Shawe-taylor, R. Zemel, P. Bartlett, F. Pereira, and K. Weinberger, Eds., 2011, pp. 1269–1277. [Online]. Available: http://books.nips.cc/papers/files/nips24/NIPS2011 0748.pdf [25] H. Zhou, L. Li, and H. Zhu, “Tensor regression with applications in neuroimaging data analysis,” Journal of American Statistical Association, vol. 108, pp. 540–552, 2013.