Multimedia Tools and Applications

, Volume 78, Issue 3, pp 3797–3816 | Cite as

Feature selection for text classification: A review

  • Xuelian Deng
  • Yuqing Li
  • Jian Weng
  • Jilian ZhangEmail author


Big multimedia data is heterogeneous in essence, that is, the data may be a mixture of video, audio, text, and images. This is due to the prevalence of novel applications in recent years, such as social media, video sharing, and location based services (LBS), etc. In many multimedia applications, for example, video/image tagging and multimedia recommendation, text classification techniques have been used extensively to facilitate multimedia data processing. In this paper, we give a comprehensive review on feature selection techniques for text classification. We begin by introducing some popular representation schemes for documents, and similarity measures used in text classification. Then, we review the most popular text classifiers, including Nearest Neighbor (NN) method, Naïve Bayes (NB), Support Vector Machine (SVM), Decision Tree (DT), and Neural Networks. Next, we survey four feature selection models, namely the filter, wrapper, embedded and hybrid, discussing pros and cons of the state-of-the-art feature selection approaches. Finally, we conclude the paper and give a brief introduction to some interesting feature selection work that does not belong to the four models.


Feature Selection Text classification Text classifiers Multimedia 



This work was supported by National Key R&D Plan of China (Grant No. 2017YFB0802203 and 2018YFB100013), National Natural Science Foundation of China (Grant Number U1736203, 61732021, 61472165, 61373158, and 61363009), Guangdong Provincial Engineering Technology Research Center on Network Security Detection and Defense (Grant No. 2014B090904067), Guangdong Provincial Special Funds for Applied Technology Research and Development and Transformation of Important Scientific and Technological Achieve (Grant No. 2016B010124009), the Zhuhai Top Discipline–Information Security, Guangzhou Key Laboratory of Data Security and Privacy Preserving, Guangdong Key Laboratory of Data Security and Privacy Preserving, National Joint Engineering Research Center of Network Security Detection and Protection Technology.


  1. 1.
    Aggarwal CC, Hinneburg A, Keim DA (2001) On the surprising behavior of distance metrics in high dimensional spaces. In: ICDT, vol 1. Springer, pp 420–434Google Scholar
  2. 2.
    Apté C, Damerau F, Weiss SM (1994) Automated learning of decision rules for text categorization. ACM Trans Inf Syst 12(3):233–251Google Scholar
  3. 3.
    Aslam JAMF (2003) An information-theoretic measure for document similarity. In: Proceedings of ACM SIGIR, pp 449–450Google Scholar
  4. 4.
    Baccianella S, Esuli A, Sebastiani F (2014) Feature selection for ordinal text classification. Neural Comput 26(3):557–591MathSciNetGoogle Scholar
  5. 5.
    Baecchi C, Uricchio T, Bertini M, Del Bimbo A (2016) A multimodal feature learning approach for sentiment analysis of social network multimedia. Multimed Tool Appl 75(5):2507–2525Google Scholar
  6. 6.
    Baeza-Yates R, Ribeiro-Neto B (1999) Modern information retrieval. ACM, New YorkGoogle Scholar
  7. 7.
    Ballan L, Bertini M, Uricchio T, Del Bimbo A (2015) Data-driven approaches for social image and video tagging. Multimed Tool Appl 74(4):1443–1468Google Scholar
  8. 8.
    Basu T, Murthy C (2016) A supervised term selection technique for effective text categorization. Int J Mach Learn Cybern 7(5):877–892Google Scholar
  9. 9.
    Bermejo P, de la Ossa L, Gámez JA, Puerta JM (2012) Fast wrapper feature subset selection in high-dimensional datasets by means of filter re-ranking. Knowl-Based Syst 25(1):35–44Google Scholar
  10. 10.
    Brown G (2009) A new perspective for information theoretic feature selection. In: Artificial intelligence and statistics, pp 49–56Google Scholar
  11. 11.
    Chandrashekar G, Sahin F (2014) A survey on feature selection methods. Comput Electr Eng 40(1):16–28Google Scholar
  12. 12.
    Chen J, Huang H, Tian S, Qu Y (2009) Feature selection for text classification with naïve bayes. Expert Syst Appl 3(36):5432–5435Google Scholar
  13. 13.
    Choi SS, Cha SH, Tappert CC (2010) A survey of binary similarity and distance measures. J Syst Cybern Inform 8(1):43–48Google Scholar
  14. 14.
    Chou CH, Sinha AP, Zhao H (2010) A hybrid attribute selection approach for text classification. J Assoc Inf Syst 11(9):491Google Scholar
  15. 15.
    Cohen WW (1995) Fast effective rule induction. In: Proceedings of the twelfth international conference on machine learning, pp 115–123Google Scholar
  16. 16.
    Combarro EF, Montanes E, Diaz I, Ranilla J, Mones R (2005) Introducing a family of linear measures for feature selection in text categorization. IEEE Trans Knowl Data Eng 17(9):1223–1232Google Scholar
  17. 17.
    Cunningham P, Delany SJ (2007) k-nearest neighbour classifiers. Multiple Class Syst 34:1–17Google Scholar
  18. 18.
    Das S (2001) Filters, wrappers and a boosting-based hybrid for feature selection. In: ICML, vol 1, pp 74–81Google Scholar
  19. 19.
    Dasgupta A, Drineas P, Harb B, Josifovski V, Mahoney MW (2007) Feature selection methods for text classification. In: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, mining. ACM, pp 230–239Google Scholar
  20. 20.
    Dash M, Liu H (1997) Feature selection for classification. Intell Data Anal 1(1-4):131–156Google Scholar
  21. 21.
    Deerwester S, Dumais ST, Furnas GW, Landauer TK, Harshman R (1990) Indexing by latent semantic analysis. J Amer Soc Inform Sci 41(6):391Google Scholar
  22. 22.
    Domingos P, Pazzani M (1997) On the optimality of the simple bayesian classifier under zero-one loss. Mach Learn 29(2/3):103–130zbMATHGoogle Scholar
  23. 23.
    Dumais S, Platt J, Heckerman D, Sahami M (1998) Inductive learning algorithms and representations for text categorization. In: Proceedings of the seventh international conference on Information and knowledge management. ACM, pp 148–155Google Scholar
  24. 24.
    Dy JG, Brodley CE (2004) Feature selection for unsupervised learning. J Mach Learn Res 5:845– 889MathSciNetzbMATHGoogle Scholar
  25. 25.
    Fang Y, Zhang J, Zhang S, Lei C, Hu X (2017) Supervised feature selection algorithm based on low-rank and manifold learning. In: Proceedings of the 13th international conference on advanced data mining and applications, ADMA 2017. Singapore, pp 273–286Google Scholar
  26. 26.
    Forman G (2003) An extensive empirical study of feature selection metrics for text classification. J Mach Learn Res 3:1289–1305zbMATHGoogle Scholar
  27. 27.
    Forman G (2004) A pitfall and solution in multi-class feature selection for text classification. In: Proceedings of the 21st international conference on machine learning. ACM, p 38Google Scholar
  28. 28.
    Fragoudis D, Meretakis D, Likothanassis S (2005) Best terms: an efficient feature-selection algorithm for text categorization. Knowl Inf Syst 8(1):16–33Google Scholar
  29. 29.
    Fu AY, Wenyin L, Deng X (2006) Detecting phishing web pages with visual similarity assessment based on earth mover’s distance (emd). IEEE Trans Dependable Secure Comput 3(4)Google Scholar
  30. 30.
    Galavotti L, Sebastiani F, Simi M (2000) Experiments on the use of feature selection and negative evidence in automated text categorization. In: International conference on theory and practice of digital libraries. Springer, pp 59–68Google Scholar
  31. 31.
    Gao B, Liu TY, Feng G, Qin T, Cheng QS, Ma WY (2005) Hierarchical taxonomy preparation for text categorization using consistent bipartite spectral graph copartitioning. IEEE Trans Knowl Data Eng 17(9):1263–1273Google Scholar
  32. 32.
    Ghareb AS, Bakar AA, Hamdan AR (2016) Hybrid feature selection based on enhanced genetic algorithm for text categorization. Expert Syst Appl 49:31–47Google Scholar
  33. 33.
    Günal S (2012) Hybrid feature selection for text classification. Turkish J Electr Eng Comput Sci 20(2):1296–1311MathSciNetGoogle Scholar
  34. 34.
    Gutlein M, Frank E, Hall M, Karwath A (2009) Large-scale attribute selection using wrappers. In: IEEE symposium on computational intelligence and data mining, 2009. CIDM’09. IEEE, pp 332–339Google Scholar
  35. 35.
    Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3(3):1157–1182zbMATHGoogle Scholar
  36. 36.
    He X, Cai D, Niyogi P (2006) Laplacian score for feature selection. In: Advances in neural information processing systems, pp 507–514Google Scholar
  37. 37.
    Hinneburg A, Aggarwal CC, Keim DA (2000) What is the nearest neighbor in high dimensional spaces? In: 26th international conference on very large databases, pp 506–515Google Scholar
  38. 38.
    Hu R, Cheng D, He W, Wen G, Zhu Y, Zhang J, Zhang S (2017) Low-rank feature selection for multi-view regression. Multimed Tool Appl 76 (16):17,479–17,495Google Scholar
  39. 39.
    Hu R, Zhu X, Cheng D, He W, Yan Y, Song J, Zhang S (2017) Graph self-representation method for unsupervised feature selection. Neurocomputing 220:130–137Google Scholar
  40. 40.
    Huang A (2008) Similarity measures for text document clustering. In: NZCSRSC, pp 49–56Google Scholar
  41. 41.
    Jain A, Zongker D (1997) Feature selection: evaluation, application, and small sample performance. IEEE Trans Pattern Anal Mach Intell 19(2):153–158Google Scholar
  42. 42.
    Jian L, Li J, Shu K, Liu H (2016) Multi-label informed feature selection. In: IJCAI, pp 1627–1633Google Scholar
  43. 43.
    Joachims T (1998) Text categorization with support vector machines: Learning with many relevant features. Mach Learn: ECML-98:137–142Google Scholar
  44. 44.
    John GH, Kohavi R, Pfleger K et al. (1994) Irrelevant features and the subset selection problem. In: Machine learning: proceedings of the eleventh international conference, pp 121–129Google Scholar
  45. 45.
    Johnson R, Zhang T (2014) Effective use of word order for text categorization with convolutional neural networks. arXiv:1412.1058
  46. 46.
    Kalousis A, Prados J, Hilario M (2007) Stability of feature selection algorithms: a study on high-dimensional spaces. Knowl Inf Syst 12(1):95–116Google Scholar
  47. 47.
    Kim SB, Han KS, Rim HC, Myaeng SH (2006) Some effective techniques for naive bayes text classification. IEEE Trans Knowl Data Eng 18(11):1457–1466Google Scholar
  48. 48.
    Kohavi R, John GH (1997) Wrappers for feature subset selection. Artif Intell 97(1-2):273–324zbMATHGoogle Scholar
  49. 49.
    Koller D, Sahami M (1996) Toward optimal feature selection. Tech. rep., Stanford InfoLabGoogle Scholar
  50. 50.
    Kullback S, Leibler RA (1951) On information and sufficiency. Ann Math Stat 22(1):79–86MathSciNetzbMATHGoogle Scholar
  51. 51.
    Lam SL, Lee DL (1999) Feature reduction for neural network based text categorization. In: Proceedings of the 6th international conference on database systems for advanced applications, 1999. IEEE, pp 195–202Google Scholar
  52. 52.
    Largeron C, Moulin C, Géry M. (2011) Entropy based feature selection for text categorization. In: Proceedings of the 2011 ACM symposium on applied computing. ACM, pp 924–928Google Scholar
  53. 53.
    Lei C, Zhu X (2017) Unsupervised feature selection via local structure learning and sparse learning.,042--017--5381--7
  54. 54.
    Levandowsky M, Winter D (1971) Distance between sets. Nature 234 (5323):34–35Google Scholar
  55. 55.
    Lewis DD, Ringuette M (1994) A comparison of two learning algorithms for text categorization. In: 3rd annual symposium on document analysis and information retrieval, vol 33, pp 81–93Google Scholar
  56. 56.
    Lin D (1998) An information-theoretic definition of similarity. In: Proceedings of international conference on machine learning, vol 98, pp 29,633–304Google Scholar
  57. 57.
    Lin Y, Lv F, Zhu S, Yang M, Cour T, Yu K, Cao L, Huang T (2011) Large-scale image classification: fast feature extraction and svm training. In: 2011 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 1689–1696Google Scholar
  58. 58.
    Lin YS, Jiang JY, Lee SJ (2014) A similarity measure for text classification and clustering. IEEE Trans Knowl Data Eng 26(7):1575–1590Google Scholar
  59. 59.
    Liu H, Setiono R (1997) Feature selection and classification-a probabilistic wrapper approach. In: Proceedings of the 9th international conference on industrial and engineering applications of AI and ES, pp 419–424Google Scholar
  60. 60.
    Liu H, Yu L (2005) Toward integrating feature selection algorithms for classification and clustering. IEEE Trans Knowl Data Eng 17(4):491–502Google Scholar
  61. 61.
    Ma Z, Nie F, Yang Y, Uijlings JR, Sebe N (2012) Web image annotation via subspace-sparsity collaborated feature selection. IEEE Trans Multim 14(4):1021–1030Google Scholar
  62. 62.
    Manku GS, Jain A, Das Sarma A (2007) Detecting near-duplicates for web crawling. In: Proceedings of the 16th international conference on World Wide Web. ACM, pp 141–150Google Scholar
  63. 63.
    McCallum A, Nigam K (1998) Employing em in poll-based active learning for text classification. In: Proceedings of the 15th international conference on machine learning, pp 350–358Google Scholar
  64. 64.
    McCallum A, Nigam K et al. (1998) A comparison of event models for naive bayes text classification. In: AAAI-98 workshop on learning for text categorization, vol 752. Madison, WI, pp 41–48Google Scholar
  65. 65.
    Mladenić D (1998) Feature subset selection in text-learning. In: European conference on machine learning, pp 95–100. SpringerGoogle Scholar
  66. 66.
    Molina LC, Belanche L, Nebot À (2002) Feature selection algorithms: a survey and experimental evaluation. In: Proceedings of 2002 IEEE international conference on data mining, 2002. ICDM 2003. IEEE, pp 306–313Google Scholar
  67. 67.
    Ng HT, Goh WB, Low KL (1997) Feature selection, perceptron learning, and a usability case study for text categorization. In: ACM SIGIR forum, vol 31. ACM, pp 67–73Google Scholar
  68. 68.
    Oh IS, Lee JS, Moon BR (2004) Hybrid genetic algorithms for feature selection. IEEE Trans Pattern Anal Mach Intell 26(11):1424–1437Google Scholar
  69. 69.
    Pappas N, Popescu-Belis A (2015) Combining content with user preferences for non-fiction multimedia recommendation: a study on ted lectures. Multi Tools Appl 74(4):1175–1197Google Scholar
  70. 70.
    Pietramala A, Policicchio VL, Rullo P, Sidhu I (2008) A genetic algorithm for text classification rule induction. In: Joint european conference on machine learning and knowledge discovery in databases. Springer, pp 188–203Google Scholar
  71. 71.
    Pudil P, Novovičová J, Kittler J (1994) Floating search methods in feature selection. Pattern Recogn Lett 15(11):1119–1125Google Scholar
  72. 72.
    Quinlan JR (1986) Induction of decision trees. Mach Learn 1(1):81–106Google Scholar
  73. 73.
    Quinlan JR (2014) C4. 5: programs for machine learning. ElsevierGoogle Scholar
  74. 74.
    Robertson SE, Walker S (1994) Some simple effective approximations to the 2-poisson model for probabilistic weighted retrieval. In: Proceedings of the 17th ACM SIGIR conference. ACM, pp 232–241Google Scholar
  75. 75.
    Rocchio JJ (1971) Relevance feedback in information retrieval. The Smart retrieval system-experiments in automatic document processingGoogle Scholar
  76. 76.
    Rogati M, Yang Y (2002) High-performing feature selection for text classification. In: Proceedings of the eleventh international conference on information and knowledge management. ACM, pp 659–661Google Scholar
  77. 77.
    Rubner Y, Tomasi C, Guibas LJ (2000) The earth mover’s distance as a metric for image retrieval. Int J Comput Vis 40(2):99–121zbMATHGoogle Scholar
  78. 78.
    Ruiz ME, Srinivasan P (2002) Hierarchical text categorization using neural networks. Inf Retr 5(1):87–118zbMATHGoogle Scholar
  79. 79.
    Schuldt C, Laptev I, Caputo B (2004) Recognizing human actions: a local svm approach. In: Proceedings of the 17th international conference on pattern recognition, 2004. ICPR 2004, vol 3. IEEE, pp 32–36Google Scholar
  80. 80.
    Schütze H, Hull DA, Pedersen JO (1995) A comparison of classifiers and document representations for the routing problem. In: Proceedings of the 18th annual international ACM SIGIR conference on research and development in information retrieval. ACM, pp 229–237Google Scholar
  81. 81.
    Scott S, Matwin S (1999) Feature engineering for text classification. In: ICML, vol 99, pp 379–388Google Scholar
  82. 82.
    Sebastiani F (2002) Machine learning in automated text cateogirzation. ACM Comput Surv 34(1):1–47MathSciNetGoogle Scholar
  83. 83.
    Song Q, Ni J, Wang G (2013) A fast clustering-based feature subset selection algorithm for high-dimensional data. IEEE Trans Knowl Data Eng 25(1):1–14Google Scholar
  84. 84.
    Strehl A, Ghosh J, Mooney R (2000) Impact of similarity measures on web-page clustering. In: Workshop on artificial intelligence for web search (AAAI 2000), vol 58, p 64Google Scholar
  85. 85.
    Strehl AJG (2000) Value-based customer grouping from large retail data-sets. In: Proceedings of SPIE, vol 4057, pp 33–42Google Scholar
  86. 86.
    Susana E, David M (2005) A novel feature selection score for text categorization. In: Proceedings of the workshop on feature selection for data mining, in conjunction with the 2005 SIAM international conference on data mining. SIAM, pp 1–8Google Scholar
  87. 87.
    Taira H, Haruno M (1999) Feature selection in svm text categorization. In: AAAI/ IAAI, pp 480–486Google Scholar
  88. 88.
    Tang B, Kay S, He H (2016) Toward optimal feature selection in naive bayes for text categorization. IEEE Trans Knowl Data Eng 28(9):2508–2521Google Scholar
  89. 89.
    Tang J, Alelyani S, Liu H (2014) Feature selection for classification: a review. Data Classification: Algorithms and Applications, p 37Google Scholar
  90. 90.
    Uuz H (2011) A two-stage feature selection method for text categorization by using information gain, principal component analysis and genetic algorithm. Knowl-Based Syst 24(7):1024–1032Google Scholar
  91. 91.
    Uysal AK (2016) An improved global feature selection scheme for text classification. Expert Syst Appl 43:82–92Google Scholar
  92. 92.
    Uysal AK, Gunal S (2012) A novel probabilistic feature selection method for text classification. Knowl-Based Syst 36:226–235Google Scholar
  93. 93.
    Uysal AK, Gunal S (2014) Text classification using genetic algorithm oriented latent semantic features. Expert Syst Appl 41(13):5938–5947Google Scholar
  94. 94.
    Vapnik VN, Vapnik V (1998) Statistical learning theory, vol 1. Wiley, New YorkzbMATHGoogle Scholar
  95. 95.
    Vergara JR, Estévez PA (2014) A review of feature selection methods based on mutual information. Neural Comput Appl 24(1):175–186Google Scholar
  96. 96.
    Wan X (2007) A novel document similarity measure based on earth mover’s distance. Inf Sci 177(18):3718–3730Google Scholar
  97. 97.
    Wan X, Peng Y (2005) The earth mover’s distance as a semantic measure for document similarity. In: Proceedings of the 14th ACM international conference on information and knowledge management. ACM, pp 301–302Google Scholar
  98. 98.
    Wang D, Zhang H, Liu R, Lv W, Wang D (2014) t-test feature selection approach based on term frequency for text categorization. Pattern Recogn Lett 45:1–10Google Scholar
  99. 99.
    Wang J, Zhao P, Hoi SC, Jin R (2014) Online feature selection and its applications. IEEE Trans Knowl Data Eng 26(3):698–710Google Scholar
  100. 100.
    Weber R, Schek HJ, Blott S (1998) A quantitative analysis and performance study for similarity-search methods in high-dimensional spaces. In: VLDB, vol 98, pp 194–205Google Scholar
  101. 101.
    Weston J, Mukherjee S, Chapelle O, Pontil M, Poggio T, Vapnik V (2001) Feature selection for svms. In: Advances in neural information processing systems, pp 668–674Google Scholar
  102. 102.
    Wiener E, Pedersen JO, Weigend AS et al. (1995) A neural network approach to topic spotting. In: Proceedings of SDAIR-95, 4th annual symposium on document analysis and information retrieval, vol 317. Las Vegas, NV, p 332Google Scholar
  103. 103.
    Wu X, Yu K, Ding W, Wang H, Zhu X (2013) Online feature selection with streaming features. IEEE Trans Pattern Anal Mach Intell 35(5):1178–1192Google Scholar
  104. 104.
    Xing EP, Jordan MI, Karp RM et al. (2001) Feature selection for high-dimensional genomic microarray data. In: ICML, vol 1, pp 601–608Google Scholar
  105. 105.
    Yan J, Liu N, Zhang B, Yan S, Chen Z, Cheng Q, Fan W, Ma WY (2005) Ocfs: optimal orthogonal centroid feature selection for text categorization. In: Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval. ACM, pp 122–129Google Scholar
  106. 106.
    Yang Y (1999) An evaluation of statistical approaches to text categorization. Inf Retr 1(1):69–90Google Scholar
  107. 107.
    Yang Y, Chute CG (1994) An example-based mapping method for text categorization and retrieval. ACM Trans Inf Syst 12(3):252–277Google Scholar
  108. 108.
    Yang Y, Pedersen JO (1997) A comparative study on feature selection in text categorization. In: ICML, vol 97, pp 412–420Google Scholar
  109. 109.
    Yang Y, Liu X (1999) A re-examination of text categorization methods. In: Proceedings of the 22nd ACM SIGIR, pp 42–49Google Scholar
  110. 110.
    Yang J, Liu Y, Zhu X, Liu Z, Zhang X (2012) A new feature selection based on comprehensive measurement both in inter-category and intra-category for text categorization. Inf Process Manag 48(4):741–754Google Scholar
  111. 111.
    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 systems.
  112. 112.
    Zhao Z, Wang L, Liu H, Ye J (2013) On similarity preserving feature selection. IEEE Trans Knowl Data Eng 25(3):619–632Google Scholar
  113. 113.
    Zhao S, Yao H, Zhao S, Jiang X, Jiang X (2016) Multi-modal microblog classification via multi-task learning. Multimed Tools Appl 75(15):8921–8938Google Scholar
  114. 114.
    Zheng Z, Wu X, Srihari R (2004) Feature selection for text categorization on imbalanced data. ACM Sigkdd Explorations Newsletter 6(1):80–89Google Scholar
  115. 115.
    Zheng W, Zhu X, Zhu Y, Hu R, Lei C (2017) Dynamic graph learning for spectral feature selection. Multimedia Tools and Applications.,042--017--5272--y
  116. 116.
    Zhu X, Zhang S, Jin Z, Zhang Z, Xu Z (2011) Missing value estimation for mixed-attribute data sets. IEEE Trans Knowl Data Eng 23(1):110–121Google Scholar
  117. 117.
    Zhu X, Zhang L, Huang Z (2014) A sparse embedding and least variance encoding approach to hashing. IEEE Trans Image Process 23(9):3737–3750MathSciNetzbMATHGoogle Scholar
  118. 118.
    Zhu X, Li X, Zhang S (2016) Block-row sparse multiview multilabel learning for image classification. IEEE Trans Cybern 46(2):450–461Google Scholar
  119. 119.
    Zhu X, Li X, Zhang S, Ju C, Wu X (2017) Robust joint graph sparse coding for unsupervised spectral feature selection. IEEE Trans Neural Netw Learn Syst 28(6):1263–1275MathSciNetGoogle Scholar
  120. 120.
    Zhu X, Li X, Zhang S, Xu Z, Yu L, Wang C (2017) Graph pca hashing for similarity search. IEEE Trans Multimed 19(9):2033–2044Google Scholar
  121. 121.
    Zhu X, Suk H, Wang L, Lee S, Shen D (2017) A novel relational regularization feature selection method for joint regression and classification in AD diagnosis. Med Image Anal 38:205–214Google Scholar
  122. 122.
    Zhu X, Suk HI, Huang H, Shen D (2017) Low-rank graph-regularized structured sparse regression for identifying genetic biomarkers. IEEE Trans Big Data 3(4):405–414Google Scholar
  123. 123.
    Zhu X, Zhang S, Hu R, Zhu Y et al (2018) Local and global structure preservation for robust unsupervised spectral feature selection. IEEE Trans Knowl Data Eng 30(3):517–529Google Scholar

Copyright information

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

Authors and Affiliations

  1. 1.College of Public Health and ManagementGuangxi University of Chinese MedicineGuangxiChina
  2. 2.College of Information Science and TechnologyJinan UniversityGuangzhouChina
  3. 3.College of Cyber SecurityJinan UniversityGuangzhouChina

Personalised recommendations