{"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/learning-deep-latent-spaces-for-multi-label","title":"Learning Deep Latent Spaces for Multi-Label Classification","arxiv_id":"1707.00418","date":"2017-07-03","proceeding":null,"authors":["Chih-Kuan Yeh","Wei-Chieh Wu","Wei-Jen Ko","Yu-Chiang Frank Wang"],"abstract":"Multi-label classification is a practical yet challenging task in machine\nlearning related fields, since it requires the prediction of more than one\nlabel category for each input instance. We propose a novel deep neural networks\n(DNN) based model, Canonical Correlated AutoEncoder (C2AE), for solving this\ntask. Aiming at better relating feature and label domain data for improved\nclassification, we uniquely perform joint feature and label embedding by\nderiving a deep latent space, followed by the introduction of label-correlation\nsensitive loss function for recovering the predicted label outputs. Our C2AE is\nachieved by integrating the DNN architectures of canonical correlation analysis\nand autoencoder, which allows end-to-end learning and prediction with the\nability to exploit label dependency. Moreover, our C2AE can be easily extended\nto address the learning problem with missing labels. Our experiments on\nmultiple datasets with different scales confirm the effectiveness and\nrobustness of our proposed method, which is shown to perform favorably against\nstate-of-the-art methods for multi-label classification.","url_abs":"http://arxiv.org/abs/1707.00418v1","url_pdf":"http://arxiv.org/pdf/1707.00418v1.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":"learning-deep-latent-spaces-for-multi-label","repo_url":"https://github.com/yankeesrules/C2AE","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"multi-label-classification-2","task_name":"MUlTI-LABEL-ClASSIFICATION"},{"task_slug":"missing-labels","task_name":"Missing Labels"},{"task_slug":"multi-label-classification","task_name":"Multi-Label Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1707.00418","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}