Advertisement

Multimedia Tools and Applications

, Volume 77, Issue 22, pp 29705–29725 | Cite as

A brief review on multi-task learning

  • Kim-Han Thung
  • Chong-Yaw Wee
Article
  • 127 Downloads

Abstract

Multi-task learning (MTL), which optimizes multiple related learning tasks at the same time, has been widely used in various applications, including natural language processing, speech recognition, computer vision, multimedia data processing, biomedical imaging, socio-biological data analysis, multi-modality data analysis, etc. MTL sometimes is also referred to as joint learning, and is closely related to other machine learning subfields like multi-class learning, transfer learning, and learning with auxiliary tasks, to name a few. In this paper, we provide a brief review on this topic, discuss the motivation behind this machine learning method, compare various MTL algorithms, review MTL methods for incomplete data, and discuss its application in deep learning. We aim to provide the readers with a simple way to understand MTL without too many complicated equations, and to help the readers to apply MTL in their applications.

Keywords

Multi-task learning MTL Transfer learning Joint learning Multi-class learning Learning with auxiliary tasks 

Notes

References

  1. 1.
    Agarwal A, Gerber S, Daume H (2010) Learning multiple tasks using manifold regularization. In: Advances in neural information processing systems. pp 46–54Google Scholar
  2. 2.
    Ahmed B, Thesen T, Blackmon K, Kuzniecky R, Devinsky O, Dy J, Brodley C (2016) Multi-task learning with weak class labels: leveraging ieeg to detect cortical lesions in cryptogenic epilepsy. In: Machine learning for healthcare conference. pp 115–133Google Scholar
  3. 3.
    Ando RK, Zhang T (2005) A framework for learning predictive structures from multiple tasks and unlabeled data. J Mach Learn Res 6(Nov):1817–1853MathSciNetzbMATHGoogle Scholar
  4. 4.
    Argyriou A (2015) Machine learning software. http://ttic.uchicago.edu/~argyriou/code/
  5. 5.
    Argyriou A, Evgeniou T, Pontil M (2007) Multi-task feature learning. In: Advances in neural information processing systems. vol 19, pp 41–48. MIT pressGoogle Scholar
  6. 6.
    Argyriou A, Evgeniou T, Pontil M (2008) Convex multi-task feature learning. Mach Learn 73(3):243–272CrossRefGoogle Scholar
  7. 7.
    Argyriou A, Micchelli CA, Pontil M, Ying Y (2008) A spectral regularization framework for multi-task structure learning, nips 20 Journal Publications on Mathematics (Harmonic Analysis)Google Scholar
  8. 8.
    Caruana R (1998) Multitask learning. In: Learning to learn, pp 95–133. SpringerGoogle Scholar
  9. 9.
    Chaichulee S, Villarroel M, Jorge J, Arteta C, Green G, McCormick K, Zisserman A, Tarassenko L (2017) Multi-task convolutional neural network for patient detection and skin segmentation in continuous non-contact vital sign monitoring. In: 2017 12th IEEE International conference on automatic face & gesture recognition (FG 2017). p 5110Google Scholar
  10. 10.
    Chen J, Liu J, Ye J (2012) Learning incoherent sparse and low-rank patterns from multiple tasks. ACM Trans Knowl Discov Data 5(4):22:1–22CrossRefGoogle Scholar
  11. 11.
    Chen J, Tang L, Liu J, Ye J (2009) A convex formulation for learning shared structures from multiple tasks. In: Proceedings of the 26th Annual International Conference on Machine Learning. pp 137–144. ACMGoogle Scholar
  12. 12.
    Chen J, Zhou J, Ye J (2011) Integrating low-rank and group-sparse structures for robust multi-task learning. In: Proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining. pp 42–50. ACMGoogle Scholar
  13. 13.
    Ciliberto C (2017) Matmtl. https://github.com/cciliber/matMTL
  14. 14.
    Ciliberto C, Mroueh Y, Poggio T (2015) Convex learning of multiple tasks and their structure. In: International conference on machine learning (ICML)Google Scholar
  15. 15.
    Collobert R, Weston J (2008) A unified architecture for natural language processing: Deep neural networks with multitask learning. In: Proceedings of the 25th international conference on machine learning. pp 160–167. ACMGoogle Scholar
  16. 16.
    Crichton G, Pyysalo S (2017) Code supporting: a neural network multi- task learning approach to biomedical named entity recognition. software,  https://doi.org/10.17863/CAM.12584
  17. 17.
    Elgammal A, Lee CS (2004) Separating style and content on a nonlinear manifold. In: Computer Vision and Pattern Recognition, 2004. CVPR 2004. Proceedings of the 2004 IEEE Computer Society Conference on. vol 1, pp I–I. IEEEGoogle Scholar
  18. 18.
    Evgeniou T, Micchelli CA, Pontil M (2005) Learning multiple tasks with kernel methods. J Mach Learn Res 6(Apr):615–637MathSciNetzbMATHGoogle Scholar
  19. 19.
    Evgeniou T, Pontil M (2004) Regularized multi–task learning. In: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining. pp 109–117. ACMGoogle Scholar
  20. 20.
    Fan J, Zhao T, Kuang Z, Zheng Y, Zhang J, Yu J, Peng J (2017) HD-MTL: hierarchical deep multi-task learning for large-scale visual recognition. IEEE Trans Image Process 26(4):1923–1938MathSciNetCrossRefGoogle Scholar
  21. 21.
    Fang Y, Ma Z, Zhang Z, Zhang XY, Bai X (2017) Dynamic multi-task learning with convolutional neural network. In: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI-17. pp 1668–1674.  https://doi.org/10.24963/ijcai.2017/231
  22. 22.
    Fazel M (2002) Matrix rank minimization with applications. Ph.D. thesis, Department of Electrical Engineering Stanford UniversityGoogle Scholar
  23. 23.
    Ghafoorian M, Karssemeijer N, Heskes T, van Uden IWM, Sanchez CI, Litjens G, de Leeuw FE, van Ginneken B, Marchiori E, Platel B (2017) Location sensitive deep convolutional neural networks for segmentation of white matter hyperintensities. Scientific Reports 7(1):5110.  https://doi.org/10.1038/s41598-017-05300-5 CrossRefGoogle Scholar
  24. 24.
    Girshick R (2015) Fast r-cnn. In: IEEE International conference on computer vision. pp 1440–1448Google Scholar
  25. 25.
    Godwin J (2018) Multi-task learning in tensorflow: Part 1. https://www.kdnuggets.com/2016/07/multi-task-learning-tensorflow-part-1.html
  26. 26.
    Gong P, Ye J, Zhang Cs (2012) Multi-stage multi-task feature learning. In: Advances in neural information processing systems. pp 1988–1996Google Scholar
  27. 27.
    Gong P, Ye J, Zhang C (2012) Robust multi-task feature learning. In: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining. pp 895–903. ACMGoogle Scholar
  28. 28.
    Gong P, Zhou J, Fan W, Ye J (2014) Efficient multi-task feature learning with calibration. In: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. pp 761–770. ACMGoogle Scholar
  29. 29.
    Han L, Zhang Y (2015) Learning tree structure in multi-task learning. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. pp 397–406. ACMGoogle Scholar
  30. 30.
    Han L, Zhang Y (2016) Multi-stage multi-task learning with reduced rank. In: AAAI. pp 1638–1644Google Scholar
  31. 31.
    Han L, Zhang Y, Song G, Xie K (2014) Encoding tree sparsity in multi-task learning: a probabilistic framework. In: AAAI. pp 1854–1860Google Scholar
  32. 32.
    Hu R, Zhu X, Cheng D, He W, Yan Y, Song J, Zhang S (2017) Graph self-representation method for unsupervised feature selection. Neurocomputing 220:130–137CrossRefGoogle Scholar
  33. 33.
    Jacob L, Vert Jp, Bach FR (2009) Clustered multi-task learning: A convex formulation. In: Advances in neural information processing systems. pp 745–752Google Scholar
  34. 34.
    Jalali A, Ravikumar P, Sanghavi S (2013) A dirty model for multiple sparse regression. IEEE Trans Inf Theory 59(12):7947–7968MathSciNetCrossRefGoogle Scholar
  35. 35.
    Jalali A, Sanghavi S, Ruan C, Ravikumar PK (2010) A dirty model for multi-task learning. In: Lafferty JD, Williams CKI, Shawe-Taylor J, Zemel R. S, Culotta A (eds) Advances in neural information processing systems 23, pp 964-972. Curran Associates, IncGoogle Scholar
  36. 36.
    Jebara T (2004) Multi-task feature and kernel selection for svms. In: Proceedings of the twenty-first international conference on Machine learning. p 55. ACMGoogle Scholar
  37. 37.
    Jebara T (2011) Multitask sparsity via maximum entropy discrimination. J Mach Learn Res 12(Jan):75–110MathSciNetzbMATHGoogle Scholar
  38. 38.
    Kim S, Xing EP (2010) Tree-guided group lasso for multi-task regression with structured sparsity. In: International conference on international conference on machine learning. pp. 543–550Google Scholar
  39. 39.
    Lee H, Battle A, Raina R, Ng AY (2007) Efficient sparse coding algorithms. In: Advances in neural information processing systems. pp 801–808Google Scholar
  40. 40.
    Lee S, Zhu J, Xing EP (2010) Adaptive multi-task lasso: with application to eqtl detection. In: Advances in neural information processing systems. pp 1306–1314Google Scholar
  41. 41.
    Li C, Gupta S, Rana S, Nguyen V, Venkatesh S, Ashley D, Livingston T (2016) Multiple adverse effects prediction in longitudinal cancer treatment. In: Pattern recognition (ICPR), 2016 23rd international conference on. pp 3156–3161. IEEEGoogle Scholar
  42. 42.
    Li X, Zhao L, Wei L, Yang MH, Wu F, Zhuang Y, Ling H, Wang J (2016) Deepsaliency: Multi-task deep neural network model for salient object detection. IEEE Trans Image Process 25(8):3919–3930MathSciNetCrossRefGoogle Scholar
  43. 43.
    Liu F, Wee CY, Chen H, Shen D (2014) Inter-modality relationship constrained multi-modality multi-task feature selection for alzheimer’s disease and mild cognitive impairment identification. NeuroImage 84:466–475CrossRefGoogle Scholar
  44. 44.
    Liu G, Yan Y, Song J, Sebe N (2014) Minimizing dataset bias: Discriminative multi-task sparse coding through shared subspace learning for image classification. In: Image processing (ICIP), 2014 IEEE international conference on. pp 2869–2873. IEEEGoogle Scholar
  45. 45.
    Liu H, Palatucci M, Zhang J (2009) Blockwise coordinate descent procedures for the multi-task lasso, with applications to neural semantic basis discovery. In: Proceedings of the 26th Annual International Conference on Machine Learning. pp 649–656. ACMGoogle Scholar
  46. 46.
    Liu J, et al. (2009) SLEP: Sparse Learning with efficient projections arizona state universityGoogle Scholar
  47. 47.
    Liu J, Ji S, Ye J (2009) Multi-task feature learning via efficient l 2, 1-norm minimization. In: Proceedings of the twenty-fifth conference on uncertainty in artificial intelligence. pp 339–348. AUAI PressGoogle Scholar
  48. 48.
    Liu J, Ye J (2009) Efficient euclidean projections in linear time. In: Proceedings of the 26th Annual International Conference on Machine Learning. pp 657–664. ACMGoogle Scholar
  49. 49.
    Liu J, Ye J (2010) Moreau-yosida regularization for grouped tree structure learning. In: Advances in neural information processing systems. pp 1459–1467Google Scholar
  50. 50.
    Liu M, Zhang J, Adeli E, Shen D (2017) Deep multi-task multi-channel learning for joint classification and regression of brain status. In: International conference on medical image computing and computer-assisted intervention. pp 3–11. SpringerGoogle Scholar
  51. 51.
    Lounici K, Pontil M, Tsybakov AB, Van De Geer S (2009)Google Scholar
  52. 52.
    Lozano AC, Swirszcz G (2012) Multi-level lasso for sparse multi-task regression. In: Proceedings of the 29th International Coference on International Conference on Machine Learning. pp 595–602. OmnipressGoogle Scholar
  53. 53.
    Mairal J, Bach F, Ponce J, Sapiro G (2009) Online dictionary learning for sparse coding. In: Proceedings of the 26th annual international conference on machine learning. pp 689–696. ACMGoogle Scholar
  54. 54.
    Mandal MK (2018) Multi-task learning in keras — implementation of multi-task classification loss. https://blog.manash.me/multi-task-learning-in-keras-implementation-of-multi-task-classification-loss-f1d42da5c3f6
  55. 55.
    Maurer A, Pontil M, Romera-Paredes B (2013) Sparse coding for multitask and transfer learning. In: International conference on machine learning. pp 343–351Google Scholar
  56. 56.
    McDonald AM, Pontil M, Stamos D (2014) Spectral k-support norm regularization. In: Advances in neural information processing systems. pp 3644–3652Google Scholar
  57. 57.
    Moeskops P, Wolterink JM, van der Velden BHM, Gilhuijs KGA, Leiner T, Viergever MA, Isgum I (2017) Deep learning for multi-task medical image segmentation in multiple modalities. CoRR arXiv:1704.03379
  58. 58.
    Negahban S, Wainwright MJ (2008) Joint support recovery under high-dimensional scaling: Benefits and perils of \(\ell _{1,\infty }\)-regularization. In: Proceedings of the 21st International Conference on Neural Information Processing Systems. pp 1161–1168. Curran Associates IncGoogle Scholar
  59. 59.
  60. 60.
    Obozinski G, Taskar B, Jordan M (2006) Multi-task feature selection. Statistics Department UC Berkeley Tech Rep2Google Scholar
  61. 61.
    Obozinski G, Taskar B, Jordan MI (2010) Joint covariate selection and joint subspace selection for multiple classification problems. Stat Comput 20(2):231–252MathSciNetCrossRefGoogle Scholar
  62. 62.
    Olshausen BA, Field DJ (1997) Sparse coding with an overcomplete basis set: a strategy employed by v1? Vis Res 37(23):3311–3325CrossRefGoogle Scholar
  63. 63.
    Pan SJ, Yang Q (2010) A survey on transfer learning. IEEE Trans Knowl Data Eng 22(10):1345–1359CrossRefGoogle Scholar
  64. 64.
    Pong TK, Tseng P, Ji S, Ye J (2010) Trace norm regularization: reformulations, algorithms, and multi-task learning. SIAM J Optim 20(6):3465–3489MathSciNetCrossRefGoogle Scholar
  65. 65.
    Ranjan R, Patel VM, Chellappa R (2017) Hyperface:A deep multi-task learning framework for face detection, landmark localization, pose estimation, and gender recognition. IEEE Transactions on Pattern Analysis and Machine IntelligenceGoogle Scholar
  66. 66.
    Rao N, Cox C, Nowak R, Rogers TT (2013) Sparse overlapping sets lasso for multitask learning and its application to fmri analysis. In: Advances in neural information processing systems. pp 2202–2210Google Scholar
  67. 67.
    Romera-Paredes B, Argyriou A, Berthouze N, Pontil M (2012) Exploiting unrelated tasks in multi-task learning. In: International conference on artificial intelligence and statistics. pp 951–959Google Scholar
  68. 68.
    Ruder S (2017) An overview of multi-task learning in deep neural networks. arXiv:1706.05098
  69. 69.
    Samala RK, Chan HP, Hadjiiski L, Helvie MA, Richter C, Cha K (2018) Cross-domain and multi-task transfer learning of deep convolutional neural network for breast cancer diagnosis in digital breast tomosynthesis. In: MICCAI. vol 10575.  https://doi.org/10.1117/12.2293412
  70. 70.
    Seltzer ML, Droppo J (2013) Multi-task learning in deep neural networks for improved phoneme recognition. In: Acoustics, speech and signal processing (ICASSP), 2013 IEEE international conference on. pp 6965–6969. IEEEGoogle Scholar
  71. 71.
    Seraj RM (2014) Multi-task learning Internet: https://www.cs.ubc.ca/~schmidtm/MLRG/multi-task%20learning.pdf
  72. 72.
    Suo Y, Dao M, Tran T, Mousavi H, Srinivas U, Monga V (2014) Group structured dirty dictionary learning for classification. In: Image processing (ICIP), 2014 IEEE international conference on. pp 150–154. IEEEGoogle Scholar
  73. 73.
    Thung KH, et al. (2014) Neurodegenerative disease diagnosis using incomplete multi-modality data via matrix shrinkage and completion. Neuroimage 91:386–400CrossRefGoogle Scholar
  74. 74.
    Tibshirani R (1996) Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society. Series B (Methodological) 58(1):267–288MathSciNetzbMATHGoogle Scholar
  75. 75.
    Titsias MK, Lázaro-Gredilla M (2011) Spike and slab variational inference for multi-task and multiple kernel learning. In: Advances in neural information processing systems. pp 2339–2347Google Scholar
  76. 76.
    Turlach BA, Venables WN, Wright SJ (2005) Simultaneous variable selection. Technometrics 47(3):349–363MathSciNetCrossRefGoogle Scholar
  77. 77.
    Vasilescu MAO, Terzopoulos D (2002) Multilinear image analysis for facial recognition. In: Pattern recognition, 2002. Proceedings. 16th international conference on. vol 2, pp 511–514. IEEEGoogle Scholar
  78. 78.
    Vogt J, Roth V (2012) A complete analysis of the l_1, p group-lasso. arXiv:1206.4632
  79. 79.
    Vounou M, Nichols TE, Montana G, Initiative ADN, et al. (2010) Discovering genetic associations with high-dimensional neuroimaging phenotypes: a sparse reduced-rank regression approach. Neuroimage 53(3):1147–1159CrossRefGoogle Scholar
  80. 80.
    Wachinger C, Reuter M, Klein T (2018) Deepnat: Deep convolutional neural network for segmenting neuroanatomy. NeuroImage 170:434–445. http://www.sciencedirect.com/science/article/pii/S1053811917301465 CrossRefGoogle Scholar
  81. 81.
    Wang H, et al. (2003) Facial expression decomposition. In: Computer vision, 2003. Proceedings. Ninth IEEE international conference on. pp 958–965. IEEEGoogle Scholar
  82. 82.
    Wang H, Nie F, Huang H, Yan J, Kim S, Risacher S, Saykin A, Shen L (2012) High-order multi-task feature learning to identify longitudinal phenotypic markers for alzheimer’s disease progression prediction. In: Advances in neural information processing systems. pp 1277–1285Google Scholar
  83. 83.
    Wang J, Ye J (2015) Safe screening for multi-task feature learning with multiple data matrices. In: International conference on machine learning. pp 1747–1756Google Scholar
  84. 84.
    Wang Z, Zhu X, Adeli E, Zhu Y, Nie F, Munsell B, Wu G (2017) Multi-modal classification of neurodegenerative disease by progressive graph-based transductive learning. Med Image Anal 39:218–230CrossRefGoogle Scholar
  85. 85.
    Weiss K, Khoshgoftaar TM, Wang D (2016) A survey of transfer learning. Journal of Big Data 3(1):9CrossRefGoogle Scholar
  86. 86.
    Wu Z, Valentini-Botinhao C, Watts O, King S (2015) Deep neural networks employing multi-task learning and stacked bottleneck features for speech synthesis. In: Acoustics, speech and signal processing (ICASSP), 2015 IEEE international conference on. pp 4460–4464. IEEEGoogle Scholar
  87. 87.
    Xiang S, Yuan L, Fan W, Wang Y, Thompson PM, Ye J, Initiative ADN, et al. (2014) Bi-level multi-source learning for heterogeneous block-wise missing data. NeuroImage 102:192–206CrossRefGoogle Scholar
  88. 88.
    Xin B, Kawahara Y, Wang Y, Hu L, Gao W (2016) Efficient generalized fused lasso and its applications. ACM Transactions on Intelligent Systems and Technology (TIST) 7(4):60Google Scholar
  89. 89.
    Xue W, Brahm G, Pandey S, Leung S, Li S (2018) Full left ventricle quantification via deep multitask relationships learning. Med Image Anal 43:54–65.  https://doi.org/10.1016/j.media.2017.09.005 CrossRefGoogle Scholar
  90. 90.
    Yan K, Zhang D, Xu Y (2017) Correcting instrumental variation and time-varying drift using parallel and serial multitask learning. IEEE Trans Instrum Meas 66(9):2306–2316CrossRefGoogle Scholar
  91. 91.
    Yuan L, et al. (2012) Multi-source feature learning for joint analysis of incomplete multiple heterogeneous neuroimaging data. NeuroImage 61(3):622–632CrossRefGoogle Scholar
  92. 92.
    Zhang C, Zhang Z (2014) Improving multiview face detection with multi-task deep convolutional neural networks. In: Applications of computer vision (WACV), 2014 IEEE winter conference on. pp 1036–1041. IEEEGoogle Scholar
  93. 93.
    Zhang D, et al. (2012) Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer’s disease. Neuroimage 59 (2):895–907CrossRefGoogle Scholar
  94. 94.
    Zhang J, Ghahramani Z, Yang Y (2006) Learning multiple related tasks using latent independent component analysis. In: Advances in neural information processing systems. pp 1585–1592Google Scholar
  95. 95.
    Zhang J, Ghahramani Z, Yang Y (2008) Flexible latent variable models for multi-task learning. Mach Learn 73(3):221–242CrossRefGoogle Scholar
  96. 96.
    Zhang J, Liang J, Hu H (2017) Multi-view texture classification using hierarchical synthetic images. Multimedia Tools and Applications 76(16):17511–17523CrossRefGoogle Scholar
  97. 97.
    Zhang J, Liu M, Shen D (2017) Detecting anatomical landmarks from limited medical imaging data using two-stage task-oriented deep neural networks. IEEE Trans Image Process 26(10):4753– 4764MathSciNetCrossRefGoogle Scholar
  98. 98.
    Zhang J, Liu M, Wang L, Chen S, Yuan P, Li J, Shen SGF, Tang Z, Chen KC, Xia JJ et al (2017) Joint craniomaxillofacial bone segmentation and landmark digitization by context-guided fully convolutional networks. In: International conference on medical image computing and computer-assisted intervention. pp 720–728. SpringerGoogle Scholar
  99. 99.
    Zhang S, Li X, Zong M, Zhu X, Wang R (2017) Efficient knn classification with different numbers of nearest neighbors IEEE transactions on neural networks and learning systemsGoogle Scholar
  100. 100.
    Zhang W, Li R, Zeng T, Sun Q, Kumar S, Ye J, Ji S (2015) Deep model based transfer and multi-task learning for biological image analysis.  https://doi.org/10.1145/2783258.2783304
  101. 101.
    Zhang Y, Yang Q (2017) A survey on multi-task learning. arXiv:1707.08114
  102. 102.
    Zhang Y, Yeung DY (2012) A convex formulation for learning task relationships in multi-task learning. arXiv:1203.3536
  103. 103.
    Zhang Z, Luo P, Loy CC, Tang X (2014) Facial landmark detection by deep multi-task learning. In: European conference on computer vision. pp 94–108. SpringerGoogle Scholar
  104. 104.
    Zheng J, Ni LM (2013) Time-dependent trajectory regression on road networks via multi-task learning. In: AAAIGoogle Scholar
  105. 105.
    Zheng W, Zhu X, Zhu Y, Hu R, Lei C (2017) Dynamic graph learning for spectral feature selection. Multimedia Tools and Applications, pp 1–17Google Scholar
  106. 106.
    Zhou J, Chen J, Ye J (2011) Malsar: Multi-task learning via structural regularization. Arizona State University 21Google Scholar
  107. 107.
    Zhou J, Liu J, Narayan VA, Ye J (2012) Modeling disease progression via fused sparse group lasso. In: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining. pp 1095–1103. ACMGoogle Scholar
  108. 108.
    Zhou J, Yuan L, Liu J, Ye J (2011) A multi-task learning formulation for predicting disease progression. In: Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining. pp 814–822. ACMGoogle Scholar
  109. 109.
    Zhou Y, Jin R, Hoi SCH (2010) Exclusive lasso for multi-task feature selection. In: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics. pp 988–995Google Scholar
  110. 110.
    Zhu X, Li X, Zhang S, Ju C, Wu X (2017) Robust joint graph sparse coding for unsupervised spectral feature selection. IEEE transactions on neural networks and learning systems 28(6):1263–1275MathSciNetCrossRefGoogle Scholar
  111. 111.
    Zhu X, Li X, Zhang S, Xu Z, Yu L, Wang C (2017) Graph pca hashing for similarity search. IEEE Transactions on Multimedia 19(9):2033–2044CrossRefGoogle Scholar
  112. 112.
    Zhu X, Suk HI, Huang H, Shen D (2016) Structured sparse low-rank regression model for brain-wide and genome-wide associations. In: International conference on medical image computing and computer-assisted intervention. pp 344–352. SpringerGoogle Scholar
  113. 113.
    Zhu X, Suk HI, Huang H, Shen D (2017) Low-rank graph-regularized structured sparse regression for identifying genetic biomarkers. IEEE Transactions on Big Data 3(4):405–414CrossRefGoogle Scholar
  114. 114.
    Zhu X, Suk HI, Lee SW, Shen D (2016) Subspace regularized sparse multitask learning for multiclass neurodegenerative disease identification. IEEE Trans Biomed Eng 63(3):607–618CrossRefGoogle Scholar
  115. 115.
    Zhu X, Zhang S, Hu R, Zhu Y et al (2017) Local and global structure preservation for robust unsupervised spectral feature selection IEEE Transactions on Knowledge and Data EngineeringGoogle Scholar
  116. 116.
    Zhu Y, Kim M, Zhu X, Yan J, Kaufer D, Wu G (2017) Personalized diagnosis for alzheimers disease. In: International conference on medical image computing and computer-assisted intervention. pp 205–213. SpringerGoogle Scholar
  117. 117.
    Zhu Y, Zhu X, Zhang H, Gao W, Shen D, Wu G (2016) Reveal consistent spatial-temporal patterns from dynamic functional connectivity for autism spectrum disorder identification. In: International conference on medical image computing and computer-assisted intervention. pp 106–114. SpringerGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of RadiologyUniversity of North CarolinaChapel HillUSA
  2. 2.Department of Biomedical EngineeringNational University of SingaporeSingaporeSingapore

Personalised recommendations