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Support Matrix Machines Cai.Jian-Feng,Candes,Emmanuel J,and Shen,Zuowei.A Liu,Ji,Musialski,Przemyslaw,Wonka,Peter,and Ye. singular value thresholding algorithm for matrix comple- Jieping.Tensor completion for estimating missing val- tion.SIAM Journal on Optimization,20(4):1956-1982, ues in visual data.In IEEE Tansactions on Pattern Anal- 2010. ysis and Machine Intelligence,volume 35,pp.208-220, 2013. Candes,Emmanuel J and Recht,Benjamin.Exact ma- trix completion via convex optimization.Foundations Nazir,M,Ishtiag,Muhammad,Batool,Anab,Jaffar,M Ar- of Computational mathematics,9(6):717-772,2009. fan,and Mirza.Anwar M.Feature selection for efficient gender classification.In Proceedings of the WSEAS in- Cortes,Corinna and Vapnik,Vladimir.Support-vector net- ternational conference,Wisconsin,pp.70-75,2010. works.Machine learning,20(3):273-297,1995. 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The Elements of Statistical Learning:Data Mining,In- Trace norm regularization:reformulations,algorithms, ference,and Prediction.Springer-Verlag,2001. and multi-task learning.SIAM Journal on Optimization, 20(6):3465-3489.2010. He,Bingsheng and Yuan,Xiaoming.On non-ergodic con- vergence rate of douglas-rachford alternating direction Salakhutdinov.Ruslan and Srebro,Nathan.Collabora- method of multipliers.Numerische Mathematik,pp.1- tive filtering in a non-uniform world:Learning with the 11,2012. weighted trace norm.arXiv preprint arXiv:1002.2780, 2010. Huang,Jin,Nie,Feiping,and Huang,Heng.Robust discrete matrix completion.In Proceedings of the Signoretto,Marco,Dinh,Quoc Tran,De Lathauwer, AAAI Conference on Artificial Intelligence,pp.424-430, Lieven,and Suykens,Johan AK.Learning with tensors: 2013. a framework based on convex optimization and spectral regularization.Machine Learning,94(3):303-351,2014. Hunyadi,Borbala,Signoretto,Marco,Van Paesschen, Srebro,Nathan and Shraibman,Adi.Rank,trace-norm and Wim,Suykens,Johan AK,Van Huffel,Sabine,and max-norm.In Proceedings of the Conference on Learn- De Vos,Maarten.Incorporating structural informa- ing Theory,pp.545-560.2005. tion from the multichannel eeg improves patient-specific seizure detection.Clinical Neurophysiology,123(12): Vandenberghe,Lieven and Boyd,Stephen.Semidefinite 2352-2361.2012. programming.SIAM review,38(1):49-95,1996. Kang,Zhuoliang,Grauman,Kristen,and Sha,Fei.Learn- Watson,G Alistair.Characterization of the subdifferential ing with whom to share in multi-task feature learning.In of some matrix norms.Linear Algebra and its Applica- Proceedings of the International Conference on Machine tions,170:33-45,1992. Learning,Pp.521-528,2011. Wolf,Lior,Jhuang.Hueihan,and Hazan,Tamir.Model- ing appearances with low-rank SVM.In Proceedings of Keerthi,S.Sathiya and Gilbert,Elmer G.Convergence of the IEEE Conference on Computer Vision and Pattern a generalized smo algorithm for svm classifier design. Recognition,pp.1-6,2007. Machine Learning,46(1-3):351-360,2002. Zheng,Wei-Long,Zhu,Jia-Yi,Peng,Yong,and Lu,Bao- Lewis,Adrian S.The mathematics of eigenvalue opti- Liang.Eeg-based emotion classification using deep be- mization.Mathematical Programming,97(1-2):155- lief networks.In Proceedings of the IEEE International 176.2003 Conference on Multimedia and Expo,pp.1-6,2014.Support Matrix Machines Cai, Jian-Feng, Candes, Emmanuel J, and Shen, Zuowei. A ` singular value thresholding algorithm for matrix comple￾tion. SIAM Journal on Optimization, 20(4):1956–1982, 2010. Candes, Emmanuel J and Recht, Benjamin. Exact ma- ` trix completion via convex optimization. Foundations of Computational mathematics, 9(6):717–772, 2009. Cortes, Corinna and Vapnik, Vladimir. Support-vector net￾works. Machine learning, 20(3):273–297, 1995. Goldstein, Tom, ODonoghue, Brendan, and Setzer, Simon. Fast alternating direction optimization methods. CAM report, pp. 12–35, 2012. Harchaoui, Zaid, Douze, Matthijs, Paulin, Mattis, Dudik, Miroslav, and Malick, Jer´ ome. Large-scale image clas- ˆ sification with trace-norm regularization. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3386–3393, 2012. Hastie, Trevor, Robert, Tibshirani, and Jerome, Friedman. The Elements of Statistical Learning: Data Mining, In￾ference, and Prediction. Springer-Verlag, 2001. He, Bingsheng and Yuan, Xiaoming. On non-ergodic con￾vergence rate of douglas–rachford alternating direction method of multipliers. Numerische Mathematik, pp. 1– 11, 2012. Huang, Jin, Nie, Feiping, and Huang, Heng. Robust discrete matrix completion. In Proceedings of the AAAI Conference on Artificial Intelligence, pp. 424–430, 2013. Hunyadi, Borbala, Signoretto, Marco, Van Paesschen, ´ Wim, Suykens, Johan AK, Van Huffel, Sabine, and De Vos, Maarten. Incorporating structural informa￾tion from the multichannel eeg improves patient-specific seizure detection. Clinical Neurophysiology, 123(12): 2352–2361, 2012. Kang, Zhuoliang, Grauman, Kristen, and Sha, Fei. Learn￾ing with whom to share in multi-task feature learning. In Proceedings of the International Conference on Machine Learning, pp. 521–528, 2011. Keerthi, S. Sathiya and Gilbert, Elmer G. Convergence of a generalized smo algorithm for svm classifier design. Machine Learning, 46(1-3):351–360, 2002. Lewis, Adrian S. The mathematics of eigenvalue opti￾mization. Mathematical Programming, 97(1-2):155– 176, 2003. Liu, Ji, Musialski, Przemyslaw, Wonka, Peter, and Ye, Jieping. Tensor completion for estimating missing val￾ues in visual data. In IEEE Tansactions on Pattern Anal￾ysis and Machine Intelligence, volume 35, pp. 208–220, 2013. Nazir, M, Ishtiaq, Muhammad, Batool, Anab, Jaffar, M Ar￾fan, and Mirza, Anwar M. Feature selection for efficient gender classification. In Proceedings of the WSEAS in￾ternational conference, Wisconsin, pp. 70–75, 2010. Nesterov, Yurii. A method of solving a convex program￾ming problem with convergence rate o(1/k2). Soviet Mathematics Doklady, 27(2):372–376, 1983. Pirsiavash, Hamed, Ramanan, Deva, and Fowlkes, Char￾less C. Bilinear classifiers for visual recognition. In Proceedings of the Advances in Neural Information Pro￾cessing Systems, pp. 1482–1490, 2009. Platt, John et al. Sequential minimal optimization: A fast algorithm for training support vector machines. Techni￾cal report msr-tr-98-14, Microsoft Research, 1998. Pong, Ting Kei, Tseng, Paul, Ji, Shuiwang, and Ye, Jieping. Trace norm regularization: reformulations, algorithms, and multi-task learning. SIAM Journal on Optimization, 20(6):3465–3489, 2010. Salakhutdinov, Ruslan and Srebro, Nathan. Collabora￾tive filtering in a non-uniform world: Learning with the weighted trace norm. arXiv preprint arXiv:1002.2780, 2010. Signoretto, Marco, Dinh, Quoc Tran, De Lathauwer, Lieven, and Suykens, Johan AK. Learning with tensors: a framework based on convex optimization and spectral regularization. Machine Learning, 94(3):303–351, 2014. Srebro, Nathan and Shraibman, Adi. Rank, trace-norm and max-norm. In Proceedings of the Conference on Learn￾ing Theory, pp. 545–560. 2005. Vandenberghe, Lieven and Boyd, Stephen. Semidefinite programming. SIAM review, 38(1):49–95, 1996. Watson, G Alistair. Characterization of the subdifferential of some matrix norms. Linear Algebra and its Applica￾tions, 170:33–45, 1992. Wolf, Lior, Jhuang, Hueihan, and Hazan, Tamir. Model￾ing appearances with low-rank SVM. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–6, 2007. Zheng, Wei-Long, Zhu, Jia-Yi, Peng, Yong, and Lu, Bao￾Liang. Eeg-based emotion classification using deep be￾lief networks. In Proceedings of the IEEE International Conference on Multimedia and Expo, pp. 1–6, 2014
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