{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T06:43:23Z","timestamp":1782369803373,"version":"3.54.5"},"reference-count":54,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2018,9,17]],"date-time":"2018-09-17T00:00:00Z","timestamp":1537142400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["World Wide Web"],"published-print":{"date-parts":[[2019,3]]},"DOI":"10.1007\/s11280-018-0637-3","type":"journal-article","created":{"date-parts":[[2018,9,17]],"date-time":"2018-09-17T04:04:11Z","timestamp":1537157051000},"page":"673-688","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Group sparse reduced rank regression for neuroimaging genetic study"],"prefix":"10.1007","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6840-0578","authenticated-orcid":false,"given":"Xiaofeng","family":"Zhu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Heung-Il","family":"Suk","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dinggang","family":"Shen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2018,9,17]]},"reference":[{"issue":"3","key":"637_CR1","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1002\/gepi.20448","volume":"34","author":"DH Ballard","year":"2010","unstructured":"Ballard, D.H., Cho, J., Zhao, H.: Comparisons of multi-marker association methods to detect association between a candiyear region and disease. Genet. Epidemiol. 34(3), 201\u2013212 (2010)","journal-title":"Genet. Epidemiol."},{"issue":"7","key":"637_CR2","doi-asserted-by":"publisher","first-page":"1765","DOI":"10.1109\/TMI.2016.2527784","volume":"35","author":"NK Batmanghelich","year":"2016","unstructured":"Batmanghelich, N.K., Dalca, A., Quon, G., Sabuncu, M., Golland, P.: Probabilistic modeling of imaging, genetics and diagnosis. IEEE Trans. Med. Imaging 35(7), 1765\u20131779 (2016)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"1","key":"637_CR3","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1038\/ng1934","volume":"39","author":"L Bertram","year":"2007","unstructured":"Bertram, L., McQueen, M.B., Mullin, K., Blacker, D., Tanzi, R.E.: Systematic meta-analyses of Alzheimer disease genetic association studies: the Alzgene database. Nat. Genet. 39(1), 17\u201323 (2007)","journal-title":"Nat. Genet."},{"key":"637_CR4","doi-asserted-by":"crossref","unstructured":"Bralten, J., Arias-V\u00e1squez, A., Makkinje, R., Veltman, J.A., Brunner, H.G., Fern\u00e1ndez, G., Rijpkema, M, Franke, B.: Association of the Alzheimer\u2019s gene SORL1 with hippocampal volume in young, healthy adults. American Journal of Psychiatry (2011)","DOI":"10.1176\/appi.ajp.2011.10101509"},{"issue":"1","key":"637_CR5","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/j.neuroimage.2009.05.022","volume":"48","author":"CC Brun","year":"2009","unstructured":"Brun, C.C., Lepor\u00e9, N., Pennec, X., Lee, A.D., Barysheva, M., Madsen, S.K., Avedissian, C., Chou, Y.-Y., Zubicaray, G.I.D., McMahon, K.L., et al.: Mapping the regional influence of genetics on brain structure variabilitya tensor-based morphometry study. Neuroimage 48(1), 37\u201349 (2009)","journal-title":"Neuroimage"},{"issue":"1","key":"637_CR6","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1080\/13554790490896938","volume":"11","author":"G Ch\u00e9telat","year":"2005","unstructured":"Ch\u00e9telat, G., Eustache, F., Viader, F., De La Sayette, V., P\u00e9lerin, A., M\u00e9zenge, F., Hannequin, D., Dupuy, B., Baron, J.-C., Desgranges, B.: FDG-PET Measurement is more accurate than neuropsychological assessments to predict global cognitive deterioration in patients with mild cognitive impairment. Neurocase 11(1), 14\u201325 (2005)","journal-title":"Neurocase"},{"issue":"1","key":"637_CR7","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1016\/S0197-4580(99)00107-4","volume":"21","author":"A Convit","year":"2000","unstructured":"Convit, A., De Asis, J., De Leon, M.J., Tarshish, C.Y., De Santi, S., Rusinek, H.: Atrophy of the medial occipitotemporal, inferior, and middle temporal gyri in non-demented elderly predict decline to alzheimers disease. Neurobiol. Aging 21 (1), 19\u201326 (2000)","journal-title":"Neurobiol. Aging"},{"key":"637_CR8","unstructured":"Deng, X., Li, Y., Weng, J., Zhang, J.: Feature selection for text classification: a review. Multimedia Tools Appl., pp. 1\u201320 (2018)"},{"key":"637_CR9","doi-asserted-by":"crossref","unstructured":"Du, L., Yan, J., Kim, S., et al.: A novel structure-aware sparse learning algorithm for brain imaging genetics. In: MICCAI, pp. 329\u2013336 (2014)","DOI":"10.1007\/978-3-319-10443-0_42"},{"key":"637_CR10","first-page":"41","volume":"19","author":"A Evgeniou","year":"2007","unstructured":"Evgeniou, A., Pontil, M.: Multi-task feature learning. NIPS 19, 41\u201348 (2007)","journal-title":"NIPS"},{"issue":"3","key":"637_CR11","doi-asserted-by":"publisher","first-page":"724","DOI":"10.1016\/j.neuroimage.2008.10.003","volume":"44","author":"N Filippini","year":"2009","unstructured":"Filippini, N., Rao, A., Wetten, S., Gibson, R.A., Borrie, M., Guzman, D., Kertesz, A., Loy-English, I., Williams, J., Nichols, T., et al.: Anatomically-distinct genetic associations of APOE\ud835\udf164 allele load with regional cortical atrophy in Alzheimer\u2019s disease. Neuroimage 44(3), 724\u2013728 (2009)","journal-title":"Neuroimage"},{"issue":"9406","key":"637_CR12","doi-asserted-by":"publisher","first-page":"392","DOI":"10.1016\/S0140-6736(04)15441-X","volume":"363","author":"NC Fox","year":"2004","unstructured":"Fox, N.C., Schott, J.M.: Imaging cerebral atrophy: normal ageing to Alzheimer\u2019s disease. The Lancet 363(9406), 392\u2013394 (2004)","journal-title":"The Lancet"},{"issue":"9","key":"637_CR13","doi-asserted-by":"publisher","first-page":"2045","DOI":"10.1109\/TMM.2017.2729019","volume":"19","author":"L Gao","year":"2017","unstructured":"Gao, L., Guo, Z., Zhang, H., Xu, X., Shen, H.T.: Video captioning with attention-based lstm and semantic consistency. IEEE Trans. Multimedia 19(9), 2045\u20132055 (2017)","journal-title":"IEEE Trans. Multimedia"},{"issue":"3","key":"637_CR14","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s00530-015-0494-1","volume":"23","author":"L Gao","year":"2017","unstructured":"Gao, L., Song, J., Liu, X., Shao, J., Liu, J., Shao, J.: Learning in high-dimensional multimedia data: the state of the art. Multimedia Syst. 23(3), 303\u2013313 (2017)","journal-title":"Multimedia Syst."},{"issue":"2","key":"637_CR15","doi-asserted-by":"publisher","first-page":"268","DOI":"10.1109\/TMI.2012.2223710","volume":"32","author":"Y Guo","year":"2013","unstructured":"Guo, Y., Wu, G., Jiang, J., Shen, D.: Robust anatomical correspondence detection by hierarchical sparse graph matching. IEEE Trans. Med. Imaging 32(2), 268\u2013277 (2013)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"637_CR16","doi-asserted-by":"crossref","unstructured":"Guo, Y., Gao, Y., Shen, D.: Deformable mr prostate segmentation via deep feature learning and sparse patch matching. In: Deep Learning for Medical Image Analysis, 197\u2013222 (2017)","DOI":"10.1016\/B978-0-12-810408-8.00012-2"},{"key":"637_CR17","doi-asserted-by":"crossref","unstructured":"Hao, X., Yu, J., Zhang, D.: Identifying genetic associations with MRI-derived measures via tree-guided sparse learning. In: MICCAI 2014, pp. 757\u2013764 (2014)","DOI":"10.1007\/978-3-319-10470-6_94"},{"issue":"4","key":"637_CR18","doi-asserted-by":"publisher","first-page":"1875","DOI":"10.1016\/j.neuroimage.2011.03.077","volume":"56","author":"DP Hibar","year":"2011","unstructured":"Hibar, D.P., Stein, J.L., Kohannim, O., Jahanshad, N., Saykin, A.J., Shen, L., Kim, S., Pankratz, N., Foroud, T., Huentelman, M.J., et al.: Voxelwise gene-wide association study (vGeneWAS): multivariate gene-based association testing in 731 elderly subjects. Neuroimage 56(4), 1875\u20131891 (2011)","journal-title":"Neuroimage"},{"key":"637_CR19","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1016\/j.neucom.2016.05.081","volume":"220","author":"R Hu","year":"2017","unstructured":"Hu, R., Zhu, X., Cheng, D., He, W., Yan, Y., Song, J., Zhang, S.: Graph self-representation method for unsupervised feature selection. Neurocomputing 220, 130\u2013137 (2017)","journal-title":"Neurocomputing"},{"key":"637_CR20","doi-asserted-by":"publisher","first-page":"613","DOI":"10.1016\/j.neuroimage.2015.05.043","volume":"118","author":"M Huang","year":"2015","unstructured":"Huang, M., Nichols, T., Huang, C., Yu, Y., Lu, Z., Knickmeyer, R.C., Feng, Q., Zhu, H.: Alzheimer\u2019s Disease Neuroimaging Initiative, et al. Fvgwas: fast voxelwise genome wide association analysis of large-scale imaging genetic data. Neuroimage 118, 613\u2013627 (2015)","journal-title":"Neuroimage"},{"issue":"2","key":"637_CR21","doi-asserted-by":"publisher","first-page":"248","DOI":"10.1016\/0047-259X(75)90042-1","volume":"5","author":"AJ Izenman","year":"1975","unstructured":"Izenman, A.J.: Reduced-rank regression for the multivariate linear model. J. Multivar. Anal. 5(2), 248\u2013264 (1975)","journal-title":"J. Multivar. Anal."},{"issue":"36","key":"637_CR22","doi-asserted-by":"publisher","first-page":"15483","DOI":"10.1073\/pnas.0901866106","volume":"106","author":"AH Joyner","year":"2009","unstructured":"Joyner, A.H., Bloss, C.S., Bakken, T.E., Rimol, L.M., Melle, I., Agartz, I., Djurovic, S., Topol, E.J., Schork, N.J., Andreassen, O.A., et al.: A common mecp2 haplotype associates with reduced cortical surface area in humans in two independent populations. Proc. Natl. Acad. Sci. 106(36), 15483\u201315488 (2009)","journal-title":"Proc. Natl. Acad. Sci."},{"key":"637_CR23","doi-asserted-by":"crossref","unstructured":"Kabani, N.J.: 3D anatomical atlas of the human brain. In: Human Brain Mapping (1998)","DOI":"10.1016\/S1053-8119(18)31550-7"},{"key":"637_CR24","doi-asserted-by":"publisher","unstructured":"Lei, C., Zhu, X.: Unsupervised feature selection via local structure learning and sparse learning. pages. \n                    https:\/\/doi.org\/10.1007\/s11042-017-5381-7\n                    \n                  , 11 (2017)","DOI":"10.1007\/s11042-017-5381-7"},{"key":"637_CR25","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1016\/j.jneumeth.2014.09.001","volume":"237","author":"D Lin","year":"2014","unstructured":"Lin, D., Cao, H., Calhoun, V.D., Wang, Y.-P.: Sparse models for correlative and integrative analysis of imaging and genetic data. J. Neurosci. Methods 237, 69\u201378 (2014)","journal-title":"J. Neurosci. Methods"},{"issue":"3","key":"637_CR26","doi-asserted-by":"publisher","first-page":"1605","DOI":"10.1016\/j.neurobiolaging.2014.12.007","volume":"36","author":"E Louwersheimer","year":"2015","unstructured":"Louwersheimer, E., Ramirez, A., Cruchaga, C., Becker, T., Kornhuber, J., Peters, O., Heilmann, S., Wiltfang, J., Jessen, F., Visser, P.J., et al.: The influence of genetic variants in SORL1 gene on the manifestation of Alzheimer\u2019s disease. Neurobiol. Aging 36(3), 1605\u2013e13 (2015)","journal-title":"Neurobiol. Aging"},{"issue":"7","key":"637_CR27","doi-asserted-by":"publisher","first-page":"1485","DOI":"10.1016\/j.neurobiolaging.2010.10.004","volume":"33","author":"JJ McCarthy","year":"2012","unstructured":"McCarthy, J.J., Saith, S., Linnertz, C., Burke, J.R., Hulette, C.M., Welsh-Bohmer, K.A., Chiba-Falek, O.: The Alzheimer\u2019s associated 5\u2019 region of the SORL1 gene cis regulates SORL1 transcripts expression. Neurobiol. Aging 33(7), 1485\u2013e1 (2012)","journal-title":"Neurobiol. Aging"},{"issue":"4","key":"637_CR28","doi-asserted-by":"publisher","first-page":"1415","DOI":"10.1016\/j.neuroimage.2008.10.031","volume":"44","author":"C Misra","year":"2009","unstructured":"Misra, C., Fan, Y., Davatzikos, C.: Baseline and longitudinal patterns of brain atrophy in MCI patients, and their use in prediction of short-term conversion to AD: results from ADNI. Neuroimage 44(4), 1415\u20131422 (2009)","journal-title":"Neuroimage"},{"issue":"2","key":"637_CR29","first-page":"191","volume":"1","author":"SL Rosenthal","year":"2012","unstructured":"Rosenthal, S.L., Wang, X., et al.: Beta-amyloid toxicity modifier genes and the risk of alzheimers disease. Am. J. Neurodegener. Dis. 1(2), 191\u2013198 (2012)","journal-title":"Am. J. Neurodegener. Dis."},{"issue":"3","key":"637_CR30","doi-asserted-by":"publisher","first-page":"1051","DOI":"10.1016\/j.neuroimage.2010.01.042","volume":"53","author":"L Shen","year":"2010","unstructured":"Shen, L., Kim, S., Risacher, S.L., Nho, K., Swaminathan, S., West, J.D., Foroud, T., Pankratz, N., Moore, J.H., Sloan, C.D., et al.: Whole genome association study of brain-wide imaging phenotypes for identifying quantitative trait loci in MCI and AD: a study of the ADNI cohort. Neuroimage 53(3), 1051\u20131063 (2010)","journal-title":"Neuroimage"},{"issue":"2","key":"637_CR31","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1007\/s11682-013-9262-z","volume":"8","author":"L Shen","year":"2014","unstructured":"Shen, L., Thompson, P.M., Potkin, S.G., et al.: Genetic analysis of quantitative phenotypes in AD and MCI: imaging, cognition and biomarkers. Brain Imaging Behav. 8(2), 183\u2013207 (2014)","journal-title":"Brain Imaging Behav."},{"key":"637_CR32","doi-asserted-by":"crossref","unstructured":"Shen, F., Xu, Y., Liu, L., Yang, Y., Huang, Z., Shen, H.T.: Unsupervised deep hashing with similarity-adaptive and discrete optimization (2018)","DOI":"10.1109\/TPAMI.2018.2789887"},{"key":"637_CR33","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1016\/j.patcog.2017.03.021","volume":"75","author":"J Song","year":"2018","unstructured":"Song, J., Gao, L., Li, L., Zhu, X., Sebe, N.: Quantization-based hashing: a general framework for scalable image and video retrieval. Pattern Recogn. 75, 175\u2013187 (2018)","journal-title":"Pattern Recogn."},{"issue":"3","key":"637_CR34","doi-asserted-by":"publisher","first-page":"1160","DOI":"10.1016\/j.neuroimage.2010.02.032","volume":"53","author":"JL Stein","year":"2010","unstructured":"Stein, J.L., Hua, X., Lee, S., Ho, A.J., Leow, A.D., Toga, A.W., Saykin, A.J., Shen, L., Foroud, T., Pankratz, N., et al.: Voxelwise genome-wide association study (vGWAS). Neuroimage 53(3), 1160\u20131174 (2010)","journal-title":"Neuroimage"},{"key":"637_CR35","doi-asserted-by":"publisher","first-page":"386","DOI":"10.1016\/j.neuroimage.2014.01.033","volume":"91","author":"K-H Thung","year":"2014","unstructured":"Thung, K.-H., Wee, C.-Y., Yap, P.-T., Shen, D.: Neurodegenerative disease diagnosis using incomplete multi-modality data via matrix shrinkage and completion. Neuroimage 91, 386\u2013400 (2014)","journal-title":"Neuroimage"},{"issue":"3","key":"637_CR36","doi-asserted-by":"publisher","first-page":"1147","DOI":"10.1016\/j.neuroimage.2010.07.002","volume":"53","author":"M Vounou","year":"2010","unstructured":"Vounou, M., Nichols, T.E., Montana, G.: ADNI discovering genetic associations with high-dimensional neuroimaging phenotypes: a sparse reduced-rank regression approach. Neuroimage 53(3), 1147\u20131159 (2010)","journal-title":"Neuroimage"},{"issue":"18","key":"637_CR37","doi-asserted-by":"publisher","first-page":"i619","DOI":"10.1093\/bioinformatics\/bts411","volume":"28","author":"H Wang","year":"2012","unstructured":"Wang, H., Nie, F., Huang, H., et al.: From phenotype to genotype: an association study of longitudinal phenotypic markers to Alzheimer\u2019s disease relevant snps. Bioinformatics 28(18), i619\u2013i625 (2012)","journal-title":"Bioinformatics"},{"issue":"2","key":"637_CR38","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1093\/bioinformatics\/btr649","volume":"28","author":"H Wang","year":"2012","unstructured":"Wang, H., Nie, F., Huang, H., et al.: Identifying quantitative trait loci via group-sparse multitask regression and feature selection: an imaging genetics study of the ADNI cohort. Bioinformatics 28(2), 229\u2013237 (2012)","journal-title":"Bioinformatics"},{"key":"637_CR39","doi-asserted-by":"crossref","unstructured":"Wang, X., Gao, L., Wang, P., Sun, X., Liu, X.: Two-stream 3d convnet fusion for action recognition in videos with arbitrary size and length. IEEE Transactions on Multimedia (2017)","DOI":"10.1109\/TMM.2017.2749159"},{"issue":"3","key":"637_CR40","doi-asserted-by":"publisher","first-page":"202","DOI":"10.1016\/j.jalz.2010.03.007","volume":"6","author":"MW Weiner","year":"2010","unstructured":"Weiner, M.W., Aisen, P.S., Jack, C.R., Jagust, W.J., Trojanowski, J.Q., Shaw, L., Saykin, A.J., Morris, J.C., Cairns, N., Beckett, L.A., et al.: The Alzheimer\u2019s disease neuroimaging initiative: progress report and future plans. Alzheimer\u2019s & Dementia 6(3), 202\u2013211 (2010)","journal-title":"Alzheimer\u2019s & Dementia"},{"issue":"11","key":"637_CR41","doi-asserted-by":"publisher","first-page":"1212","DOI":"10.1038\/mp.2013.146","volume":"19","author":"K Xia","year":"2014","unstructured":"Xia, K., Guo, H., Hu, Z., et al.: Common genetic variants on 1p13. 2 associate with risk of autism. Mol. Psychiatry 19(11), 1212\u20131219 (2014)","journal-title":"Mol. Psychiatry"},{"issue":"5","key":"637_CR42","doi-asserted-by":"publisher","first-page":"1774","DOI":"10.1109\/TNNLS.2017.2673241","volume":"29","author":"S Zhang","year":"2018","unstructured":"Zhang, S., Li, X., Zong, M., Zhu, X., Wang, R.: Efficient knn classification with different numbers of nearest neighbors. IEEE Trans. Neural Netw. Learn. Syst. 29(5), 1774\u20131785 (2018)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"637_CR43","doi-asserted-by":"publisher","unstructured":"Zheng, W., Zhu, X., Zhu, Y., Hu, R., Lei, C.: Dynamic graph learning for spectral feature selection. Multimedia Tools and Applications, pages. \n                    https:\/\/doi.org\/10.1007\/s11042-017-5272-y\n                    \n                   (2017)","DOI":"10.1007\/s11042-017-5272-y"},{"key":"637_CR44","doi-asserted-by":"publisher","unstructured":"Zheng, W., Zhu, X., Wen, G., Zhu, Y., Yu, H., Gan, J.: Unsupervised feature selection by self-paced learning regularization. Pattern Recognition Letters, page. \n                    https:\/\/doi.org\/10.1016\/j.patrec.2018.06.029\n                    \n                   (2018)","DOI":"10.1016\/j.patrec.2018.06.029"},{"key":"637_CR45","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1016\/j.neuroimage.2014.05.078","volume":"100","author":"X Zhu","year":"2014","unstructured":"Zhu, X., Suk, H.-I., Shen, D.: A novel matrix-similarity based loss function for joint regression and classification in AD diagnosis. Neuroimage 100, 91\u2013105 (2014)","journal-title":"Neuroimage"},{"issue":"9","key":"637_CR46","doi-asserted-by":"publisher","first-page":"3737","DOI":"10.1109\/TIP.2014.2332764","volume":"23","author":"X Zhu","year":"2014","unstructured":"Zhu, X., Zhang, L., Zi, H.: A sparse embedding and least variance encoding approach to hashing. IEEE Trans. Image Process. 23(9), 3737\u20133750 (2014)","journal-title":"IEEE Trans. Image Process."},{"issue":"3","key":"637_CR47","doi-asserted-by":"publisher","first-page":"607","DOI":"10.1109\/TBME.2015.2466616","volume":"63","author":"X Zhu","year":"2016","unstructured":"Zhu, X., Suk, H.-I., Lee, S.-W., Shen, D.: Subspace regularized sparse multitask learning for multiclass neurodegenerative disease identification. IEEE Trans. Biomed. Eng. 63(3), 607\u2013618 (2016)","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"2","key":"637_CR48","doi-asserted-by":"publisher","first-page":"450","DOI":"10.1109\/TCYB.2015.2403356","volume":"46","author":"X Zhu","year":"2016","unstructured":"Zhu, X., Li, X., Zhang, S.: Block-row sparse multiview multilabel learning for image classification. IEEE Trans. Cybernet. 46(2), 450\u2013461 (2016)","journal-title":"IEEE Trans. Cybernet."},{"issue":"6","key":"637_CR49","doi-asserted-by":"publisher","first-page":"1263","DOI":"10.1109\/TNNLS.2016.2521602","volume":"28","author":"X Zhu","year":"2017","unstructured":"Zhu, X., Li, X., Zhang, S., Ju, C., Wu, X.: Robust joint graph sparse coding for unsupervised spectral feature selection. IEEE Trans. Neural Netw. Learn. Syst. 28 (6), 1263\u20131275 (2017)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"9","key":"637_CR50","doi-asserted-by":"publisher","first-page":"2033","DOI":"10.1109\/TMM.2017.2703636","volume":"19","author":"X Zhu","year":"2017","unstructured":"Zhu, X., Li, X., Zhang, S., Xu, Z., Yu, L., Wang, C.: Graph pca hashing for similarity search. IEEE Trans. Multimedia 19(9), 2033\u20132044 (2017)","journal-title":"IEEE Trans. Multimedia"},{"issue":"4","key":"637_CR51","doi-asserted-by":"publisher","first-page":"405","DOI":"10.1109\/TBDATA.2017.2735991","volume":"3","author":"X Zhu","year":"2017","unstructured":"Zhu, X., Suk, H.-I., Huang, H., Shen, D.: Low-rank graph-regularized structured sparse regression for identifying genetic biomarkers. IEEE Trans. Big Data 3(4), 405\u2013414 (2017)","journal-title":"IEEE Trans. Big Data"},{"key":"637_CR52","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1016\/j.media.2015.10.008","volume":"38","author":"X Zhu","year":"2017","unstructured":"Zhu, X., Suk, H.-I., Wang, L., Lee, S.-W., Shen, D.: A novel relational regularization feature selection method for joint regression and classification in AD diagnosis. Med. Image Anal. 38, 205\u2013214 (2017)","journal-title":"Med. Image Anal."},{"key":"637_CR53","doi-asserted-by":"publisher","unstructured":"Zhu, X., Zhang, S., Li, Y., Zhang, J., Yang, L., Fang, Y.: Low-rank sparse subspace for spectral clustering. IEEE Transactions on Knowledge and Data Engineering (2018). \n                    https:\/\/doi.org\/10.1109\/TKDE.2018.2858782","DOI":"10.1109\/TKDE.2018.2858782"},{"issue":"3","key":"637_CR54","doi-asserted-by":"publisher","first-page":"517","DOI":"10.1109\/TKDE.2017.2763618","volume":"30","author":"X Zhu","year":"2018","unstructured":"Zhu, X., Zhang, S., Hu, R., Zhu, Y., et al.: Local and global structure preservation for robust unsupervised spectral feature selection. IEEE Trans. Knowl. Data Eng. 30(3), 517\u2013529 (2018)","journal-title":"IEEE Trans. Knowl. Data Eng."}],"container-title":["World Wide Web"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11280-018-0637-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11280-018-0637-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11280-018-0637-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,9,16]],"date-time":"2019-09-16T19:05:52Z","timestamp":1568660752000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11280-018-0637-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,9,17]]},"references-count":54,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2019,3]]}},"alternative-id":["637"],"URL":"https:\/\/doi.org\/10.1007\/s11280-018-0637-3","relation":{},"ISSN":["1386-145X","1573-1413"],"issn-type":[{"value":"1386-145X","type":"print"},{"value":"1573-1413","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,9,17]]},"assertion":[{"value":"9 March 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 July 2018","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 September 2018","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 September 2018","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}