{"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/dismec-distributed-sparse-machines-for","title":"DiSMEC - Distributed Sparse Machines for Extreme Multi-label Classification","arxiv_id":"1609.02521","date":"2016-09-08","proceeding":null,"authors":["Rohit Babbar","Bernhard Shoelkopf"],"abstract":"Extreme multi-label classification refers to supervised multi-label learning\ninvolving hundreds of thousands or even millions of labels. Datasets in extreme\nclassification exhibit fit to power-law distribution, i.e. a large fraction of\nlabels have very few positive instances in the data distribution. Most\nstate-of-the-art approaches for extreme multi-label classification attempt to\ncapture correlation among labels by embedding the label matrix to a\nlow-dimensional linear sub-space. However, in the presence of power-law\ndistributed extremely large and diverse label spaces, structural assumptions\nsuch as low rank can be easily violated.\n  In this work, we present DiSMEC, which is a large-scale distributed framework\nfor learning one-versus-rest linear classifiers coupled with explicit capacity\ncontrol to control model size. Unlike most state-of-the-art methods, DiSMEC\ndoes not make any low rank assumptions on the label matrix. Using double layer\nof parallelization, DiSMEC can learn classifiers for datasets consisting\nhundreds of thousands labels within few hours. The explicit capacity control\nmechanism filters out spurious parameters which keep the model compact in size,\nwithout losing prediction accuracy. We conduct extensive empirical evaluation\non publicly available real-world datasets consisting upto 670,000 labels. We\ncompare DiSMEC with recent state-of-the-art approaches, including - SLEEC which\nis a leading approach for learning sparse local embeddings, and FastXML which\nis a tree-based approach optimizing ranking based loss function. On some of the\ndatasets, DiSMEC can significantly boost prediction accuracies - 10% better\ncompared to SLECC and 15% better compared to FastXML, in absolute terms.","url_abs":"http://arxiv.org/abs/1609.02521v1","url_pdf":"http://arxiv.org/pdf/1609.02521v1.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":"dismec-distributed-sparse-machines-for","repo_url":"https://github.com/Refefer/fastxml","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"dismec-distributed-sparse-machines-for","repo_url":"https://github.com/xmc-aalto/dismec","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":"extreme-multi-label-classification","task_name":"Extreme Multi-Label Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"multi-label-classification-2","task_name":"MUlTI-LABEL-ClASSIFICATION"},{"task_slug":"multi-label-classification","task_name":"Multi-Label Classification"},{"task_slug":"multi-label-learning","task_name":"Multi-Label Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1609.02521","atlas_url":"https://app.syntology.ai/?focus=1609.02521","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1609.02521"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+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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