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This calls for\nalternative criteria one can compute on non-labeled data. In this paper, two\ncriteria that do not require labels are empirically shown to discriminate\naccurately (w.r.t. ROC or PR based criteria) between algorithms. These criteria\nare based on existing Excess-Mass (EM) and Mass-Volume (MV) curves, which\ngenerally cannot be well estimated in large dimension. A methodology based on\nfeature sub-sampling and aggregating is also described and tested, extending\nthe use of these criteria to high-dimensional datasets and solving major\ndrawbacks inherent to standard EM and MV curves.","url_abs":"http://arxiv.org/abs/1607.01152v1","url_pdf":"http://arxiv.org/pdf/1607.01152v1.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":"how-to-evaluate-the-quality-of-unsupervised","repo_url":"https://github.com/ngoix/EMMV_benchmarks","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"how-to-evaluate-the-quality-of-unsupervised","repo_url":"https://github.com/bstienen/unsupervised-learning-metrics","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"anomaly-detection","task_name":"Anomaly Detection"},{"task_slug":"supervised-anomaly-detection","task_name":"Supervised Anomaly Detection"},{"task_slug":"unsupervised-anomaly-detection","task_name":"Unsupervised Anomaly Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1607.01152","atlas_url":"https://app.syntology.ai/?focus=1607.01152","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1607.01152"}},"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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