{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T03:40:13Z","timestamp":1772941213441,"version":"3.50.1"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"18","license":[{"start":{"date-parts":[[2023,12,2]],"date-time":"2023-12-02T00:00:00Z","timestamp":1701475200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,2]],"date-time":"2023-12-02T00:00:00Z","timestamp":1701475200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100021171","name":"Basic and Applied Basic Research Foundation of Guangdong Province","doi-asserted-by":"publisher","award":["2022A1515010688"],"award-info":[{"award-number":["2022A1515010688"]}],"id":[{"id":"10.13039\/501100021171","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-023-17669-9","type":"journal-article","created":{"date-parts":[[2023,12,2]],"date-time":"2023-12-02T07:02:12Z","timestamp":1701500532000},"page":"55889-55902","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Incomplete multi-view clustering via diffusion completion"],"prefix":"10.1007","volume":"83","author":[{"given":"Sifan","family":"Fang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3646-2066","authenticated-orcid":false,"given":"Zuyuan","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Junhang","family":"Chen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,12,2]]},"reference":[{"key":"17669_CR1","doi-asserted-by":"crossref","unstructured":"Huang H, Zhou G, Liang N, Zhao Q, Xie S (2022) Diverse deep matrix factorization with hypergraph regularization for multiview data representation. IEEE\/CAA J Automatica Sinica","DOI":"10.1109\/JAS.2022.105980"},{"key":"17669_CR2","doi-asserted-by":"crossref","unstructured":"Huang H, Zhou G, Zhao Q, He L, Xie S (2023) Comprehensive multiview representation learning via deep autoencoder-like nonnegative matrix factorization. IEEE Trans Neural Networks and Learn Syst","DOI":"10.1109\/TNNLS.2023.3304626"},{"key":"17669_CR3","first-page":"2148","volume":"34","author":"E Pan","year":"2021","unstructured":"Pan E, Kang Z (2021) Multi-view contrastive graph clustering. Adv Neural Inf Process Syst 34:2148\u20132159","journal-title":"Adv Neural Inf Process Syst"},{"key":"17669_CR4","doi-asserted-by":"publisher","unstructured":"Liang N, Yang Z, Chen J, Li Z, Xie S (2023) Label-weighted graph-based learning for semi-supervised classification under label noise. IEEE Trans Big Data:1\u201311. https:\/\/doi.org\/10.1109\/TBDATA.2023.3319249","DOI":"10.1109\/TBDATA.2023.3319249"},{"key":"17669_CR5","unstructured":"Liu X, Liu L, Liao Q, Wang S, Zhang Y, Tu W, Tang C, Liu J, Zhu E (2021) One pass late fusion multi-view clustering. In: International conference on machine learning, PMLR, pp 6850\u20136859"},{"key":"17669_CR6","unstructured":"Peng X, Huang Z, Lv J, Zhu H, Zhou JT (2019) Comic: multi-view clustering without parameter selection. In: International conference on machine learning, PMLR, pp 5092\u20135101"},{"key":"17669_CR7","doi-asserted-by":"crossref","unstructured":"Chen M-S, Huang L, Wang C-D, Huang D (2020) Multi-view clustering in latent embedding space. In: Proceedings of the AAAI conference on artificial intelligence, vol 34, pp 3513\u20133520","DOI":"10.1609\/aaai.v34i04.5756"},{"issue":"6","key":"17669_CR8","doi-asserted-by":"publisher","first-page":"6504","DOI":"10.1109\/TKDE.2022.3171911","volume":"35","author":"N Liang","year":"2023","unstructured":"Liang N, Yang Z, Xie S (2023) Incomplete multi-view clustering with sample-level auto-weighted graph fusion. IEEE Trans Knowl Data Eng 35(6):6504\u20136511. https:\/\/doi.org\/10.1109\/TKDE.2022.3171911","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"17669_CR9","doi-asserted-by":"crossref","unstructured":"Lin Y, Gou Y, Liu X, Bai J, Lv J, Peng X (2022) Dual contrastive prediction for incomplete multi-view representation learning. IEEE Trans Pattern Anal Mach Intell","DOI":"10.1109\/TPAMI.2022.3197238"},{"key":"17669_CR10","doi-asserted-by":"crossref","unstructured":"Wen J, Zhang Z, Xu Y, Zhang B, Fei L, Xie, G-S (2021) Cdimc-net: cognitive deep incomplete multi-view clustering network. In: Proceedings of the twenty-ninth international conference on international joint conferences on artificial intelligence, pp 3230\u20133236","DOI":"10.24963\/ijcai.2020\/447"},{"key":"17669_CR11","doi-asserted-by":"crossref","unstructured":"Zhang Y, Liu X, Wang S, Liu J, Dai S, Zhu E (2021) One-stage incomplete multi-view clustering via late fusion. In: Proceedings of the 29th ACM international conference on multimedia, pp 2717\u20132725","DOI":"10.1145\/3474085.3475204"},{"key":"17669_CR12","doi-asserted-by":"crossref","unstructured":"Wen J, Zhang Z, Zhang Z, Zhu L, Fei L, Zhang B, Xu Y (2021) Unified tensor framework for incomplete multi-view clustering and missing-view inferring. In: Proceedings of the AAAI conference on artificial intelligence, vol 35, pp 10273\u201310281","DOI":"10.1609\/aaai.v35i11.17231"},{"key":"17669_CR13","doi-asserted-by":"crossref","unstructured":"Xu J, Li C, Ren Y, Peng L, Mo Y, Shi X, Zhu X (2022) Deep incomplete multi-view clustering via mining cluster complementarity. In: Proceedings of the AAAI conference on artificial intelligence, vol 36, pp 8761\u20138769","DOI":"10.1609\/aaai.v36i8.20856"},{"key":"17669_CR14","unstructured":"Tang H, Liu Y (2022) Deep safe incomplete multi-view clustering: theorem and algorithm. In: International conference on machine learning, PMLR, pp 21090\u201321110"},{"key":"17669_CR15","doi-asserted-by":"publisher","first-page":"1771","DOI":"10.1109\/TIP.2020.3048626","volume":"30","author":"Q Wang","year":"2021","unstructured":"Wang Q, Ding Z, Tao Z, Gao Q, Fu Y (2021) Generative partial multi-view clustering with adaptive fusion and cycle consistency. IEEE Trans Image Process 30:1771\u20131783","journal-title":"IEEE Trans Image Process"},{"key":"17669_CR16","doi-asserted-by":"crossref","unstructured":"Li S-Y, Jiang Y, Zhou Z-H (2014) Partial multi-view clustering. In: Proceedings of the AAAI conference on artificial intelligence, vol 28","DOI":"10.1609\/aaai.v28i1.8973"},{"key":"17669_CR17","doi-asserted-by":"crossref","unstructured":"Wen J, Wu Z, Zhang Z, Fei L, Zhang B, Xu Y (2021) Structural deep incomplete multi-view clustering network. In: Proceedings of the 30th ACM international conference on information & knowledge management, pp 3538\u20133542","DOI":"10.1145\/3459637.3482192"},{"key":"17669_CR18","doi-asserted-by":"crossref","unstructured":"Xu C, Guan Z, Zhao W, Wu H, Niu Y, Ling B (2019) Adversarial incomplete multi-view clustering. In: IJCAI, vol 7, pp 3933\u20133939","DOI":"10.24963\/ijcai.2019\/546"},{"key":"17669_CR19","doi-asserted-by":"crossref","unstructured":"Lin Y, Gou Y, Liu Z, Li B, Lv J, Peng X (2021) Completer: incomplete multi-view clustering via contrastive prediction. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 11174\u201311183","DOI":"10.1109\/CVPR46437.2021.01102"},{"key":"17669_CR20","unstructured":"Zhao H, Liu H, Fu Y (2016) Incomplete multi-modal visual data grouping. In: IJCAI, pp 2392\u20132398"},{"key":"17669_CR21","doi-asserted-by":"crossref","unstructured":"Wang Q, Tao Z, Xia W, Gao Q, Cao X, Jiao L (2022) Adversarial multiview clustering networks with adaptive fusion. IEEE Trans neural Netw Learn Syst","DOI":"10.1109\/TNNLS.2022.3145048"},{"key":"17669_CR22","unstructured":"Sohl-Dickstein J, Weiss E, Maheswaranathan N, Ganguli S (2015) Deep unsupervised learning using nonequilibrium thermodynamics. In: International conference on machine learning, PMLR, pp 2256\u20132265"},{"key":"17669_CR23","first-page":"6840","volume":"33","author":"J Ho","year":"2020","unstructured":"Ho J, Jain A, Abbeel P (2020) Denoising diffusion probabilistic models. Adv Neural Inf Process Syst 33:6840\u20136851","journal-title":"Adv Neural Inf Process Syst"},{"key":"17669_CR24","unstructured":"Song Y, Ermon S (2019) Generative modeling by estimating gradients of the data distribution. Adv Neural Inf Process Syst 32"},{"key":"17669_CR25","doi-asserted-by":"crossref","unstructured":"Rombach R, Blattmann A, Lorenz D, Esser P, Ommer B (2022) High-resolution image synthesis with latent diffusion models. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 10684\u201310695","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"17669_CR26","first-page":"8780","volume":"34","author":"P Dhariwal","year":"2021","unstructured":"Dhariwal P, Nichol A (2021) Diffusion models beat gans on image synthesis. Adv Neural Inf Process Syst 34:8780\u20138794","journal-title":"Adv Neural Inf Process Syst"},{"key":"17669_CR27","unstructured":"Kong Z, Ping W, Huang J, Zhao K, Catanzaro B (2020) Diffwave: a versatile diffusion model for audio synthesis. arXiv:2009.09761"},{"key":"17669_CR28","unstructured":"Chen N, Zhang Y, Zen H, Weiss RJ, Norouzi M, Chan W (2020) Wavegrad: estimating gradients for waveform generation. arXiv:2009.00713"},{"key":"17669_CR29","unstructured":"Ramesh A, Dhariwal P, Nichol A, Chu C, Chen M (2022) Hierarchical text-conditional image generation with clip latents. arXiv:2204.06125"},{"key":"17669_CR30","doi-asserted-by":"crossref","unstructured":"Saharia C, Chan W, Saxena S, Li L, Whang J, Denton E, Ghasemipour SKS, Ayan BK, Mahdavi SS, Lopes RG, et al (2022) Photorealistic text-to-image diffusion models with deep language understanding. arXiv:2205.11487","DOI":"10.1145\/3528233.3530757"},{"key":"17669_CR31","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/j.neucom.2022.01.029","volume":"479","author":"H Li","year":"2022","unstructured":"Li H, Yang Y, Chang M, Chen S, Feng H, Xu Z, Li Q, Chen Y (2022) Srdiff: single image super-resolution with diffusion probabilistic models. Neurocomputing 479:47\u201359","journal-title":"Neurocomputing"},{"key":"17669_CR32","doi-asserted-by":"crossref","unstructured":"Saharia C, Ho J, Chan W, Salimans T, Fleet DJ, Norouzi M (2022) Image super-resolution via iterative refinement. IEEE Trans Pattern Anal Mach Intell","DOI":"10.1109\/TPAMI.2022.3204461"},{"key":"17669_CR33","unstructured":"Brock A, Donahue J, Simonyan K (2018) Large scale gan training for high fidelity natural image synthesis. arXiv:1809.11096"},{"key":"17669_CR34","doi-asserted-by":"crossref","unstructured":"Liu K, Tang W, Zhou F, Qiu G (2019) Spectral regularization for combating mode collapse in gans. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 6382\u20136390","DOI":"10.1109\/ICCV.2019.00648"},{"key":"17669_CR35","unstructured":"Ho J, Salimans T (2022) Classifier-free diffusion guidance. arXiv:2207.12598"},{"key":"17669_CR36","unstructured":"Nichol A, Dhariwal P, Ramesh A, Shyam P, Mishkin P, McGrew B, Sutskever I, Chen M (2021) Glide: towards photorealistic image generation and editing with text-guided diffusion models. arXiv:2112.10741"},{"key":"17669_CR37","doi-asserted-by":"crossref","unstructured":"Xu J, Ren Y, Tang H, Pu X, Zhu X, Zeng M, He L (2021) Multi-vae: learning disentangled view-common and view-peculiar visual representations for multi-view clustering. In: Proceedings of the IEEE\/CVF international conference on computer vision (ICCV), pp 9234\u20139243","DOI":"10.1109\/ICCV48922.2021.00910"},{"key":"17669_CR38","doi-asserted-by":"crossref","unstructured":"Xu J, Tang H, Ren Y, Peng L, Zhu X, He L (2022) Multi-level feature learning for contrastive multi-view clustering. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 16051\u201316060","DOI":"10.1109\/CVPR52688.2022.01558"},{"key":"17669_CR39","doi-asserted-by":"crossref","unstructured":"Saharia C, Chan W, Chang H, Lee C, Ho J, Salimans T, Fleet D, Norouzi M (2022) Palette: image-to-image diffusion models. In: ACM SIGGRAPH 2022 conference proceedings, pp 1\u201310","DOI":"10.1145\/3528233.3530757"},{"key":"17669_CR40","doi-asserted-by":"crossref","unstructured":"Pinaya WH, Tudosiu P-D, Dafflon J, Da\u00a0Costa PF, Fernandez V, Nachev P, Ourselin S, Cardoso MJ (2022) Brain imaging generation with latent diffusion models. In: Deep Generative Models: second MICCAI workshop, DGM4MICCAI 2022, held in conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings, Springer, pp 117\u2013126","DOI":"10.1007\/978-3-031-18576-2_12"},{"key":"17669_CR41","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I (2017) Attention is all you need. Adv Neural Inf Process Syst 30"},{"key":"17669_CR42","doi-asserted-by":"crossref","unstructured":"Li Y, Hu P, Liu Z, Peng D, Zhou JT, Peng X (2021) Contrastive clustering. In: Proceedings of the AAAI conference on artificial intelligence, vol 35, pp 8547\u20138555","DOI":"10.1609\/aaai.v35i10.17037"},{"key":"17669_CR43","first-page":"5000","volume":"34","author":"JZ HaoChen","year":"2021","unstructured":"HaoChen JZ, Wei C, Gaidon A, Ma T (2021) Provable guarantees for self-supervised deep learning with spectral contrastive loss. Adv Neural Inf Process Syst 34:5000\u20135011","journal-title":"Adv Neural Inf Process Syst"},{"key":"17669_CR44","doi-asserted-by":"crossref","unstructured":"Van\u00a0Gansbeke W, Vandenhende S, Georgoulis S, Proesmans M, Van\u00a0Gool L (2020) Scan: learning to classify images without labels. In: Computer vision\u2013ECCV 2020: 16th European conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part X, Springer, pp 268\u2013285","DOI":"10.1007\/978-3-030-58607-2_16"},{"key":"17669_CR45","doi-asserted-by":"crossref","unstructured":"Esser P, Rombach R, Ommer B (2021) Taming transformers for high-resolution image synthesis. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 12873\u201312883","DOI":"10.1109\/CVPR46437.2021.01268"},{"key":"17669_CR46","unstructured":"Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv:1711.05101"},{"key":"17669_CR47","unstructured":"Xiao H, Rasul K, Vollgraf R (2017) Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms. arXiv:1708.07747"},{"key":"17669_CR48","unstructured":"Nene SA, Nayar SK, Murase H, et al (1996) Columbia object image library (coil-20)"},{"key":"17669_CR49","unstructured":"Wang W, Arora R, Livescu K, Bilmes J (2015) On deep multi-view representation learning. In: International Conference on Machine Learning, PMLR, pp 1083\u20131092"},{"issue":"11","key":"17669_CR50","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278\u20132324","journal-title":"Proc IEEE"},{"key":"17669_CR51","unstructured":"Maaten L, Hinton G (2008) Visualizing data using t-sne. J Mach Learn Res 9(11)"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-17669-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-023-17669-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-17669-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,15]],"date-time":"2024-05-15T10:34:46Z","timestamp":1715769286000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-023-17669-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,2]]},"references-count":51,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2024,5]]}},"alternative-id":["17669"],"URL":"https:\/\/doi.org\/10.1007\/s11042-023-17669-9","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,2]]},"assertion":[{"value":"6 June 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 October 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 November 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 December 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interest"}}]}}