{"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/10000-times-accelerated-robust-subset","title":"10,000+ Times Accelerated Robust Subset Selection (ARSS)","arxiv_id":"1409.3660","date":"2014-09-12","proceeding":null,"authors":["Feiyun Zhu","Bin Fan","Xinliang Zhu","Ying Wang","Shiming Xiang","Chunhong Pan"],"abstract":"Subset selection from massive data with noised information is increasingly\npopular for various applications. This problem is still highly challenging as\ncurrent methods are generally slow in speed and sensitive to outliers. To\naddress the above two issues, we propose an accelerated robust subset selection\n(ARSS) method. Specifically in the subset selection area, this is the first\nattempt to employ the $\\ell_{p}(0<p\\leq1)$-norm based measure for the\nrepresentation loss, preventing large errors from dominating our objective. As\na result, the robustness against outlier elements is greatly enhanced.\nActually, data size is generally much larger than feature length, i.e. $N\\gg\nL$. Based on this observation, we propose a speedup solver (via ALM and\nequivalent derivations) to highly reduce the computational cost, theoretically\nfrom $O(N^{4})$ to $O(N{}^{2}L)$. Extensive experiments on ten benchmark\ndatasets verify that our method not only outperforms state of the art methods,\nbut also runs 10,000+ times faster than the most related method.","url_abs":"http://arxiv.org/abs/1409.3660v4","url_pdf":"http://arxiv.org/pdf/1409.3660v4.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":[],"tasks":[{"task_slug":"10-shot-image-generation","task_name":"10-shot image generation"},{"task_slug":"action-recognition-in-videos","task_name":"Action Recognition"},{"task_slug":"collaborative-filtering","task_name":"Collaborative Filtering"},{"task_slug":"fine-grained-image-classification","task_name":"Fine-Grained Image Classification"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"image-generation","task_name":"Image Generation"},{"task_slug":"machine-translation","task_name":"Machine Translation"},{"task_slug":"multimodal-sentiment-analysis","task_name":"Multimodal Sentiment Analysis"},{"task_slug":"music-modeling","task_name":"Music Modeling"},{"task_slug":"named-entity-recognition-ner","task_name":"Named Entity Recognition (NER)"},{"task_slug":"nested-named-entity-recognition","task_name":"Nested Named Entity Recognition"},{"task_slug":"node-classification","task_name":"Node Classification"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"panoptic-segmentation","task_name":"Panoptic Segmentation"},{"task_slug":"part-of-speech-tagging","task_name":"Part-Of-Speech Tagging"},{"task_slug":"pose-estimation","task_name":"Pose Estimation"},{"task_slug":"salient-object-detection","task_name":"RGB Salient Object Detection"},{"task_slug":"relation-extraction","task_name":"Relation Extraction"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"semi-supervised-image-classification","task_name":"Semi-Supervised Image Classification"},{"task_slug":"skeleton-based-action-recognition","task_name":"Skeleton Based Action Recognition"},{"task_slug":"temporal-action-proposal-generation","task_name":"Temporal Action Proposal Generation"},{"task_slug":"traffic-prediction","task_name":"Traffic Prediction"},{"task_slug":"visual-object-tracking","task_name":"Visual Object Tracking"}],"methods":[{"method_slug":"speed","method_name":"SPEED"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/multimodal-sentiment-analysis-on-cmu-mosi","task":"Multimodal Sentiment Analysis","dataset":"CMU-MOSI","model":"MCEN","rank_in_archive_order":2,"of":12,"metrics":{"Acc-2":"87.35","Acc-5":"58.02","Acc-7":"50.58","Corr":"0.813","F1":"87.48","MAE":"0.678"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}