Robust feature-sample linear discriminant analysis for brain disorders diagnosis

Advances in Neural Information Processing Systems

Volumn 2015-January, Issue , 2015, Pages 658-666

  Adeli Mosabbeb, Ehsan ^a   Thung, Kim Han ^a   An, Le ^a   Shi, Feng ^a   Shen, Dinggang ^a  

^a UNIVERSITY OF NORTH CAROLINA SCHOOL OF MEDICINE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DIAGNOSIS; DISCRIMINANT ANALYSIS; FEATURE EXTRACTION; INFORMATION SCIENCE; LEAST SQUARES APPROXIMATIONS; NEUROIMAGING; STATISTICS;

AREA UNDER THE ROC CURVE; CLASSIFICATION METHODS; DISCRIMINATIVE METHODS; DISCRIMINATIVE MODELS; DIVERSE APPLICATIONS; LEAST-SQUARES FORMULATION; LINEAR DISCRIMINANT ANALYSIS; STATE-OF-THE-ART METHODS;

NEURODEGENERATIVE DISEASES;

EID: 84965153630     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (35)

1. E. Adeli-Mosabbeb and M. Fathy. Non-negative matrix completion for action detection. Image Vision Comput., 39:38-51, 2015.
2. N. Bissantz, L. Dümbgen, A. Munk, and B. Stratmann. Convergence analysis of generalized iteratively reweighted least squares algorithms on convex function spaces. SIAM Optimiz., 19(4):1828-1845, 2009.
3. S. Boyd and et al.. Distributed optimization and statistical learning via the alternating direction method of multipliers. Found. Trends Mach. Learn., 3(1):1-122, 2011.
4. Heiko Braak, Kelly Tredici, Udo Rub, Rob de Vos, Ernst Jansen Steur, and Eva Braak. Staging of brain pathology related to sporadic parkinsons disease. Neurobio. of Aging, 24(2):197-211, 2003.
5. D. Cai, X. He, and J. Han. Semi-supervised discriminant analysis. In CVPR, 2007.
6. J.-F. Cai, E. Candès, and Z. Shen. A singular value thresholding algorithm for matrix completion. SIAM Optimiz., 20(4):1956-1982, 2010.
7. E. Candès, X. Li, Y. Ma, and J. Wright. Robust principal component analysis? J. ACM, 58(3), 2011.
8. O. Chapelle, B. Schölkopf, and A. Zien, editors. Semi-Supervised Learning. MIT Press, 2006.
9. C. Croux and C. Dehon. Robust linear discriminant analysis using s-estimators. Canadian J. of Statistics, 29(3):473-493, 2001.
10. F. De la Torre. A least-squares framework for component analysis. IEEE TPAMI, 34(6):1041-1055, 2012.
11. E. Elhamifar and R. Vidal. Robust classification using structured sparse representation. In CVPR, 2011.
12. S. Fidler, D. Skocaj, and A. Leonardis. Combining reconstructive and discriminative subspace methods for robust classification and regression by subsampling. IEEE TPAMI, 28(3):337-350, 2006.
13. V. Fritsch, G. Varoquaux, B. Thyreau, J.-B. Poline, and B. Thirion. Detecting outliers in high-dimensional neuroimaging datasets with robust covariance estimators. Med. Image Anal., 16(7):1359-1370, 2012.
14. A. Goldberg, X. Zhu, B. Recht, J.-M. Xu, and R. Nowak. Transduction with matrix completion: Three birds with one stone. In NIPS, pages 757-765, 2010.
15. D. Huang, R. Cabral, and F. De la Torre. Robust regression. In ECCV, pages 616-630, 2012.
16. A. Joulin and F. Bach. A convex relaxation for weakly supervised classifiers. In ICML, 2012.
17. S. Kim, A. Magnani, and S. Boyd. Robust Fisher discriminant analysis. In NIPS, pages 659-666, 2005.
18. H. Li, T. Jiang, and K. Zhang. Efficient and robust feature extraction by maximum margin criterion. In NIPS, pages 97-104, 2003.
19. H. Li, C. Shen, A. van den Hengel, and Q. Shi. Worst-case linear discriminant analysis as scalable semidefinite feasibility problems. IEEE TIP, 24(8), 2015.
20. K. O. Lim and A. Pfefferbaum. Segmentation of MR brain images into cerebrospinal fluid spaces, white and gray matter. J. of Computer Assisted Tomography, 13:588-593, 1989.
21. G. Liu, Z. Lin, S. Yan, J. Sun, Y. Yu, and Y. Ma. Robust recovery of subspace structures by low-rank representation. IEEE TPAMI, 35(1):171-184, 2013.
22. C. Lu, J. Shi, and J. Jia. Online robust dictionary learning. In CVPR, pages 415-422, June 2013.
23. K. Marek and et al.. The parkinson progression marker initiative (PPMI). Prog. Neurobiol., 95(4):629-635, 2011.
24. B. Pearce, A. Palmer, D. Bowen, G. Wilcock, M. Esiri, and A. Davison. Neurotransmitter dysfunction and atrophy of the caudate nucleus in alzheimer's disease. Neurochem Pathol., 2(4):221-32, 1985.
25. D. Shen and C. Davatzikos. HAMMER: Hierarchical attribute matching mechanism for elastic registration. IEEE TMI, 21:1421-1439, 2002.
26. K.-H. Thung, C.-Y. Wee, P.-T. Yap, and D. Shen. Neurodegenerative disease diagnosis using incomplete multi-modality data via matrix shrinkage and completion. NeuroImage, 91:386-400, 2014.
27. N. Tzourio-Mazoyer and et al.. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. NeuroImage, 15(1):273-289, 2002.
28. A. Wagner, J. Wright, A. Ganesh, Zihan Zhou, and Yi Ma. Towards a practical face recognition system: Robust registration and illumination by sparse representation. In CVPR, pages 597-604, 2009.
29. Y. Wang, J. Nie, P.-T. Yap, G. Li, F. Shi, X. Geng, L. Guo, D. Shen, ADNI, et al. Knowledge-guided robust MRI brain extraction for diverse large-scale neuroimaging studies on humans and non-human primates. PLOS ONE, 9(1):e77810, 2014.
30. Y. Wang, J. Nie, P.-T. Yap, F. Shi, L. Guo, and D. Shen. Robust deformable-surface-based skull-stripping for large-scale studies. In MICCAI, volume 6893, pages 635-642, 2011.
31. A. Worker and et al.. Cortical thickness, surface area and volume measures in parkinson's disease, multiple system atrophy and progressive supranuclear palsy. PLOS ONE, 9(12), 2014.
32. D. Zhang, Y. Wang, L. Zhou, H. Yuan, D. Shen, ADNI, et al. Multimodal classification of Alzheimer's disease and mild cognitive impairment. NeuroImage, 55(3):856-867, 2011.
33. Y. Zhang, M. Brady, and S. Smith. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE TMI, 20(1):45-57, 2001.
34. Xiaojin Zhu. Semi-supervised learning literature survey. Technical Report 1530, Computer Sciences, University of Wisconsin-Madison, 2005.
35. D. Ziegler and J. Augustinack. Harnessing advances in structural MRI to enhance research on Parkinson's disease. Imaging Med., 5(2):91-94, 2013.