{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/information-recovery-driven-deep-incomplete","title":"Information Recovery-Driven Deep Incomplete Multiview Clustering Network","arxiv_id":"2304.00429","date":"2023-04-02","proceeding":null,"authors":["Chengliang Liu","Jie Wen","Zhihao Wu","Xiaoling Luo","Chao Huang","Yong Xu"],"abstract":"Incomplete multi-view clustering is a hot and emerging topic. It is well known that unavoidable data incompleteness greatly weakens the effective information of multi-view data. To date, existing incomplete multi-view clustering methods usually bypass unavailable views according to prior missing information, which is considered as a second-best scheme based on evasion. Other methods that attempt to recover missing information are mostly applicable to specific two-view datasets. To handle these problems, in this paper, we propose an information recovery-driven deep incomplete multi-view clustering network, termed as RecFormer. Concretely, a two-stage autoencoder network with the self-attention structure is built to synchronously extract high-level semantic representations of multiple views and recover the missing data. Besides, we develop a recurrent graph reconstruction mechanism that cleverly leverages the restored views to promote the representation learning and the further data reconstruction. Visualization of recovery results are given and sufficient experimental results confirm that our RecFormer has obvious advantages over other top methods.","url_abs":"https://arxiv.org/abs/2304.00429v5","url_pdf":"https://arxiv.org/pdf/2304.00429v5.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"information-recovery-driven-deep-incomplete","repo_url":"https://github.com/justsmart/RecFormer","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"information-recovery-driven-deep-incomplete","repo_url":"https://github.com/justsmart/Recformer-mindspore","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"mindspore","reach":{"status":"ok"}}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"graph-reconstruction","task_name":"Graph Reconstruction"},{"task_slug":"incomplete-multi-view-clustering","task_name":"Incomplete multi-view clustering"},{"task_slug":"multiview-clustering","task_name":"Multiview Clustering"},{"task_slug":"representation-learning","task_name":"Representation Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2304.00429","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.00429"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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