{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T17:51:26Z","timestamp":1740160286882,"version":"3.37.3"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2024,8,11]],"date-time":"2024-08-11T00:00:00Z","timestamp":1723334400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,8,11]],"date-time":"2024-08-11T00:00:00Z","timestamp":1723334400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2024,12]]},"DOI":"10.1007\/s13042-024-02295-0","type":"journal-article","created":{"date-parts":[[2024,8,11]],"date-time":"2024-08-11T07:01:51Z","timestamp":1723359711000},"page":"5963-5979","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Label distribution learning via second-order self-representation"],"prefix":"10.1007","volume":"15","author":[{"given":"Peiqiu","family":"Yu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weiwei","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9879-9855","authenticated-orcid":false,"given":"Xiuyi","family":"Jia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,8,11]]},"reference":[{"issue":"8","key":"2295_CR1","doi-asserted-by":"publisher","first-page":"1819","DOI":"10.1109\/TKDE.2013.39","volume":"26","author":"ML Zhang","year":"2014","unstructured":"Zhang ML, Zhou ZH (2014) A review on multi-label learning algorithms. IEEE Trans Knowl Data Eng 26(8):1819\u20131837","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"7","key":"2295_CR2","doi-asserted-by":"publisher","first-page":"1734","DOI":"10.1109\/TKDE.2016.2545658","volume":"28","author":"X Geng","year":"2016","unstructured":"Geng X (2016) Label distribution learning. IEEE Trans Knowl Data Eng 28(7):1734\u20131748","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"2295_CR3","first-page":"834","volume":"30","author":"W Shen","year":"2017","unstructured":"Shen W, Zhao K, Guo Y, Yuille AL (2017) Label distribution learning forests. Adv Neural Inf Process Syst 30:834\u2013843","journal-title":"Adv Neural Inf Process Syst"},{"doi-asserted-by":"crossref","unstructured":"Wang J, Geng X (2019) Theoretical analysis of label distribution learning. In: AAAI conference on artificial intelligence, pp 5256\u20135263","key":"2295_CR4","DOI":"10.1609\/aaai.v33i01.33015256"},{"doi-asserted-by":"crossref","unstructured":"Wang J, Geng X (2019) Classification with label distribution learning. In: International joint conference on artificial intelligence, pp 3712\u20133718","key":"2295_CR5","DOI":"10.24963\/ijcai.2019\/515"},{"issue":"4","key":"2295_CR6","doi-asserted-by":"publisher","first-page":"1632","DOI":"10.1109\/TKDE.2019.2947040","volume":"33","author":"N Xu","year":"2021","unstructured":"Xu N, Liu YP, Geng X (2021) Label enhancement for label distribution learning. IEEE Trans Knowl Data Eng 33(4):1632\u20131643","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"12","key":"2295_CR7","doi-asserted-by":"publisher","first-page":"15364","DOI":"10.1109\/TPAMI.2023.3300310","volume":"45","author":"Y Lu","year":"2023","unstructured":"Lu Y, Li W, Li H, Jia X (2023) Predicting label distribution from tie-allowed multi-label ranking. IEEE Trans Pattern Anal Mach Intell 45(12):15364\u201315379","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2295_CR8","doi-asserted-by":"publisher","first-page":"322","DOI":"10.1016\/j.ins.2022.05.094","volume":"607","author":"W Qian","year":"2022","unstructured":"Qian W, Ye Q, Li Y, Huang J, Dai S (2022) Relevance-based label distribution feature selection via convex optimization. Inf Sci 607:322\u2013345","journal-title":"Inf Sci"},{"issue":"2","key":"2295_CR9","doi-asserted-by":"publisher","first-page":"839","DOI":"10.1109\/TNNLS.2021.3103178","volume":"34","author":"J Wang","year":"2023","unstructured":"Wang J, Geng X (2023) Label distribution learning by exploiting label distribution manifold. IEEE Trans Neural Netw Learn Syst 34(2):839\u2013852","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"doi-asserted-by":"crossref","unstructured":"Shao R, Xu N, Geng X (2018) Multi-label learning with label enhancement. In: IEEE international conference on data mining, pp 437\u2013446","key":"2295_CR10","DOI":"10.1109\/ICDM.2018.00059"},{"issue":"1","key":"2295_CR11","first-page":"39","volume":"22","author":"AL Berger","year":"1996","unstructured":"Berger AL, Pietra VJD, Pietra SAD (1996) A maximum entropy approach to natural language processing. Comput Linguist 22(1):39\u201371","journal-title":"Comput Linguist"},{"doi-asserted-by":"crossref","unstructured":"Lyons MJ (2021) \u201cExcavating AI\u201d re-excavated: debunking a fallacious account of the Jaffe dataset. arXiv:2107.13998","key":"2295_CR12","DOI":"10.31234\/osf.io\/bvf2s"},{"doi-asserted-by":"crossref","unstructured":"Lyons M, Akamatsu S, Kamachi M, Gyoba J (1998) Coding facial expressions with gabor wavelets. In: IEEE international conference on automatic face and gesture recognition, pp 200\u2013205","key":"2295_CR13","DOI":"10.1109\/AFGR.1998.670949"},{"issue":"3\u20134","key":"2295_CR14","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1080\/02699939208411068","volume":"6","author":"P Ekman","year":"1992","unstructured":"Ekman P (1992) An argument for basic emotions. Cogn Emot 6(3\u20134):169\u2013200","journal-title":"Cogn Emot"},{"doi-asserted-by":"crossref","unstructured":"Geng X, Xia Y (2014) Head pose estimation based on multivariate label distribution. In: IEEE conference on computer vision and pattern recognition, pp 1837\u20131842","key":"2295_CR15","DOI":"10.1109\/CVPR.2014.237"},{"issue":"9","key":"2295_CR16","doi-asserted-by":"publisher","first-page":"1757","DOI":"10.1016\/j.patcog.2004.03.009","volume":"37","author":"MR Boutell","year":"2004","unstructured":"Boutell MR, Luo J, Shen X, Brown CM (2004) Learning multi-label scene classification. Pattern Recogn 37(9):1757\u20131771","journal-title":"Pattern Recogn"},{"unstructured":"Yin L, Wei X, Sun Y, Wang J, Rosato M (2006) A 3D facial expression database for facial behavior research. In: International conference on automatic face and gesture recognition, pp 211\u2013216","key":"2295_CR17"},{"doi-asserted-by":"crossref","unstructured":"Lyons M, Akamatsu S, Kamachi M, Gyoba J (1998) Coding facial expressions with gabor wavelets. In: IEEE international conference on automatic face and gesture recognition, pp 200\u2013205","key":"2295_CR18","DOI":"10.1109\/AFGR.1998.670949"},{"issue":"12","key":"2295_CR19","doi-asserted-by":"publisher","first-page":"2037","DOI":"10.1109\/TPAMI.2006.244","volume":"28","author":"T Ahonen","year":"2006","unstructured":"Ahonen T, Hadid A, Pietikainen M (2006) Face description with local binary patterns: application to face recognition. IEEE Trans Pattern Anal Mach Intell 28(12):2037\u20132041","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"25","key":"2295_CR20","doi-asserted-by":"publisher","first-page":"14863","DOI":"10.1073\/pnas.95.25.14863","volume":"95","author":"MB Eisen","year":"1998","unstructured":"Eisen MB, Spellman PT, Brown PO, Botstein D (1998) Cluster analysis and display of genome-wide expression patterns. Natl Acad Sci 95(25):14863\u201314868","journal-title":"Natl Acad Sci"},{"issue":"10","key":"2295_CR21","doi-asserted-by":"publisher","first-page":"2401","DOI":"10.1109\/TPAMI.2013.51","volume":"35","author":"X Geng","year":"2013","unstructured":"Geng X, Yin C, Zhou ZH (2013) Facial age estimation by learning from label distributions. IEEE Trans Pattern Anal Mach Intell 35(10):2401\u20132412","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"2","key":"2295_CR22","first-page":"1695","volume":"35","author":"X Jia","year":"2023","unstructured":"Jia X, Shen X, Li W, Lu Y, Zhu J (2023) Label distribution learning by maintaining label ranking relation. IEEE Trans Knowl Data Eng 35(2):1695\u20131707","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"2295_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.106690","volume":"213","author":"X Liu","year":"2021","unstructured":"Liu X, Zhu J, Zheng Q, Li Z, Liu R, Wang J (2021) Bidirectional loss function for label enhancement and distribution learning. Knowl-Based Syst 213:106690","journal-title":"Knowl-Based Syst"},{"doi-asserted-by":"crossref","unstructured":"Su K, Geng X (2019) Soft facial landmark detection by label distribution learning. In: AAAI conference on artificial intelligence, pp 5008\u20135015","key":"2295_CR24","DOI":"10.1609\/aaai.v33i01.33015008"},{"key":"2295_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11704-020-8272-4","volume":"15","author":"H Zhang","year":"2021","unstructured":"Zhang H, Zhang Y, Geng X (2021) Practical age estimation using deep label distribution learning. Front Comput Sci 15:1\u20136","journal-title":"Front Comput Sci"},{"doi-asserted-by":"crossref","unstructured":"Gao BB, Zhou HY, Wu J, Geng X (2018) Age estimation using expectation of label distribution learning. In: International joint conference on artificial intelligence, pp 712\u2013718","key":"2295_CR26","DOI":"10.24963\/ijcai.2018\/99"},{"doi-asserted-by":"crossref","unstructured":"Wen X, Li B, Guo H, Liu Z, Hu G, Tang M, Wang J (2020) Adaptive variance based label distribution learning for facial age estimation. In: European conference on computer vision, pp 379\u2013395","key":"2295_CR27","DOI":"10.1007\/978-3-030-58592-1_23"},{"doi-asserted-by":"crossref","unstructured":"Huo Z, Geng X (2017) Ordinal zero-shot learning. In: International joint conference on artificial intelligence, pp 1916\u20131922","key":"2295_CR28","DOI":"10.24963\/ijcai.2017\/266"},{"doi-asserted-by":"crossref","unstructured":"Zhou D, Zhang X, Zhou Y, Zhao Q, Geng X (2016) Emotion distribution learning from texts. In: Conference on empirical methods in natural language processing, pp 638\u2013647","key":"2295_CR29","DOI":"10.18653\/v1\/D16-1061"},{"doi-asserted-by":"crossref","unstructured":"Zhang Y, Fu J, She D, Zhang Y, Wang S, Yang J (2018) Text emotion distribution learning via multi-task convolutional neural network. In: International joint conference on artificial intelligence, pp 4595\u20134601","key":"2295_CR30","DOI":"10.24963\/ijcai.2018\/639"},{"doi-asserted-by":"crossref","unstructured":"Xiong H, Liu H, Zhong B, Fu Y (2019) Structured and sparse annotations for image emotion distribution learning. In: AAAI conference on artificial intelligence, pp 363\u2013370","key":"2295_CR31","DOI":"10.1609\/aaai.v33i01.3301363"},{"key":"2295_CR32","doi-asserted-by":"publisher","first-page":"302","DOI":"10.1007\/s11704-018-8015-y","volume":"13","author":"M Ling","year":"2019","unstructured":"Ling M, Geng X (2019) Soft video parsing by label distribution learning. Front Comput Sci 13:302\u2013317","journal-title":"Front Comput Sci"},{"doi-asserted-by":"crossref","unstructured":"Liao L, Zhang X, Zhao F, Lou J, Wang L, Xu X, Zhang H, Li G (2020) Multi-branch deformable convolutional neural network with label distribution learning for fetal brain age prediction. In: International symposium on biomedical imaging, pp 424\u2013427","key":"2295_CR33","DOI":"10.1109\/ISBI45749.2020.9098553"},{"issue":"1","key":"2295_CR34","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1109\/TII.2021.3075989","volume":"18","author":"J Chen","year":"2022","unstructured":"Chen J, Guo C, Xu R, Zhang K, Yang Z, Liu H (2022) Toward children\u2019s empathy ability analysis: joint facial expression recognition and intensity estimation using label distribution learning. IEEE Trans Ind Inf 18(1):16\u201325","journal-title":"IEEE Trans Ind Inf"},{"key":"2295_CR35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11704-021-0611-6","volume":"16","author":"Y Ren","year":"2022","unstructured":"Ren Y, Xu N, Ling M, Geng X (2022) Label distribution for multimodal machine learning. Front Comput Sci 16:1\u201311","journal-title":"Front Comput Sci"},{"issue":"4","key":"2295_CR36","doi-asserted-by":"publisher","first-page":"380","DOI":"10.1109\/34.588021","volume":"19","author":"S Della Pietra","year":"1997","unstructured":"Della Pietra S, Della Pietra V, Lafferty J (1997) Inducing features of random fields. IEEE Trans Pattern Anal Mach Intell 19(4):380\u2013393","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"unstructured":"Yang X, Geng X, Zhou D (2016) Sparsity conditional energy label distribution learning for age estimation. In: International joint conference on artificial intelligence, pp 2259\u20132265","key":"2295_CR37"},{"unstructured":"Geng X, Hou P (2015) Pre-release prediction of crowd opinion on movies by label distribution learning. In: International joint conference on artificial intelligence, pp 3511\u20133517","key":"2295_CR38"},{"issue":"6","key":"2295_CR39","doi-asserted-by":"publisher","first-page":"2825","DOI":"10.1109\/TIP.2017.2689998","volume":"26","author":"BB Gao","year":"2017","unstructured":"Gao BB, Xing C, Xie CW, Wu J, Geng X (2017) Deep label distribution learning with label ambiguity. IEEE Trans Image Process 26(6):2825\u20132838","journal-title":"IEEE Trans Image Process"},{"issue":"4","key":"2295_CR40","doi-asserted-by":"publisher","first-page":"1619","DOI":"10.1109\/TKDE.2019.2943337","volume":"33","author":"X Jia","year":"2021","unstructured":"Jia X, Li Z, Zheng X, Li W, Huang SJ (2021) Label distribution learning with label correlations on local samples. IEEE Trans Knowl Data Eng 33(4):1619\u20131631","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"8","key":"2295_CR41","doi-asserted-by":"publisher","first-page":"3846","DOI":"10.1109\/TIP.2017.2655445","volume":"26","author":"Z He","year":"2017","unstructured":"He Z, Li X, Zhang Z, Wu F, Geng X, Zhang Y, Yang MH, Zhuang Y (2017) Data-dependent label distribution learning for age estimation. IEEE Trans Image Process 26(8):3846\u20133858","journal-title":"IEEE Trans Image Process"},{"key":"2295_CR42","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1016\/j.sigpro.2018.06.001","volume":"152","author":"A Liu","year":"2018","unstructured":"Liu A, Shi Y, Jing P, Liu J, Su Y (2018) Structured low-rank inverse-covariance estimation for visual sentiment distribution prediction. Signal Process 152:206\u2013216","journal-title":"Signal Process"},{"issue":"4","key":"2295_CR43","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/rs13040755","volume":"13","author":"J Luo","year":"2021","unstructured":"Luo J, Wang Y, Ou Y, He B, Li B (2021) Neighbor-based label distribution learning to model label ambiguity for aerial scene classification. Remote Sens 13(4):1\u201324","journal-title":"Remote Sens"},{"key":"2295_CR44","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1016\/j.ijar.2020.02.003","volume":"121","author":"S Xu","year":"2020","unstructured":"Xu S, Ju H, Shang L, Pedrycz W, Yang X, Li C (2020) Label distribution learning: a local collaborative mechanism. Int J Approx Reason 121:59\u201384","journal-title":"Int J Approx Reason"},{"issue":"2","key":"2295_CR45","first-page":"539","volume":"33","author":"R Li","year":"2022","unstructured":"Li R, Zhu J, Liu X (2022) Label distribution learning with collaboration among labels. J Softw 33(2):539\u2013554","journal-title":"J Softw"},{"doi-asserted-by":"crossref","unstructured":"Zhai Y, Dai J (2019) Geometric mean metric learning for label distribution learning. In: Neural information processing, pp 260\u2013272","key":"2295_CR46","DOI":"10.1007\/978-3-030-36711-4_23"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02295-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-024-02295-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02295-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,29]],"date-time":"2024-10-29T10:30:04Z","timestamp":1730197804000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-024-02295-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,11]]},"references-count":46,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2024,12]]}},"alternative-id":["2295"],"URL":"https:\/\/doi.org\/10.1007\/s13042-024-02295-0","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"type":"print","value":"1868-8071"},{"type":"electronic","value":"1868-808X"}],"subject":[],"published":{"date-parts":[[2024,8,11]]},"assertion":[{"value":"6 June 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 July 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 August 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}