{"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/label-embedded-dictionary-learning-for-image","title":"Label Embedded Dictionary Learning for Image Classification","arxiv_id":"1903.03087","date":"2019-03-07","proceeding":null,"authors":["Shuai Shao","Yan-Jiang Wang","Bao-Di Liu","Weifeng Liu","Rui Xu"],"abstract":"Recently, label consistent k-svd (LC-KSVD) algorithm has been successfully\napplied in image classification. The objective function of LC-KSVD is consisted\nof reconstruction error, classification error and discriminative sparse codes\nerror with L0-norm sparse regularization term. The L0-norm, however, leads to\nNP-hard problem. Despite some methods such as orthogonal matching pursuit can\nhelp solve this problem to some extent, it is quite difficult to find the\noptimum sparse solution. To overcome this limitation, we propose a label\nembedded dictionary learning (LEDL) method to utilise the L1-norm as the sparse\nregularization term so that we can avoid the hard-to-optimize problem by\nsolving the convex optimization problem. Alternating direction method of\nmultipliers and blockwise coordinate descent algorithm are then exploited to\noptimize the corresponding objective function. Extensive experimental results\non six benchmark datasets illustrate that the proposed algorithm has achieved\nsuperior performance compared to some conventional classification algorithms.","url_abs":"http://arxiv.org/abs/1903.03087v2","url_pdf":"http://arxiv.org/pdf/1903.03087v2.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":"label-embedded-dictionary-learning-for-image","repo_url":"https://github.com/The-Shuai/Label-Embedded-Dictionary-Learning","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"dictionary-learning","task_name":"Dictionary Learning"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"image-classification","task_name":"image-classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}