{"url":"/sota/dimensionality-reduction-on-mca","task":{"name":"Dimensionality Reduction","url":"/task/dimensionality-reduction","note":null},"dataset":{"name":"MCA","url":null},"category":"Computer Vision","categories":["Computer Vision","Methodology"],"category_note":null,"description":"Dimensionality reduction is the task of reducing the dimensionality of a dataset.\r\n\r\n<span style=\"color:grey; opacity: 0.6\">( Image credit: [openTSNE](https://github.com/pavlin-policar/openTSNE) )</span>","description_from":"task","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","rank":"the archive's row order at snapshot; not re-ranked","rows_end_at":"2025-07-28","rows_withheld_as_spam":0,"metric_values":"the archive's strings, untouched"},"metrics":["Classification Accuracy"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"Classification Accuracy":"higher"}},"counts":{"rows":4,"rows_with_code":3,"rows_with_paper_page":4,"rows_dated":4,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"UDRN","metrics":{"Classification Accuracy":"90.9"},"uses_additional_data":false,"paper_date":"2022-07-08","paper":"/paper/udrn-unified-dimensional-reduction-neural","paper_url":"https://arxiv.org/abs/2207.03809v2","paper_title":"UDRN: Unified Dimensional Reduction Neural Network for Feature Selection and Feature Projection","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":2,"model":"tSNE","metrics":{"Classification Accuracy":"51.5"},"uses_additional_data":false,"paper_date":"2008-11-01","paper":"/paper/visualizing-data-using-t-sne","paper_url":"https://www.jmlr.org/papers/v9/vandermaaten08a.html","paper_title":"Visualizing Data using t-SNE","code":"https://github.com/elki-project/elki","n_code_links":1,"syntology":null},{"rank_in_archive_order":3,"model":"IVIS","metrics":{"Classification Accuracy":"46.6"},"uses_additional_data":false,"paper_date":"2020-09-27","paper":"/paper/parametric-umap-learning-embeddings-with-deep","paper_url":"https://arxiv.org/abs/2009.12981v4","paper_title":"Parametric UMAP embeddings for representation and semi-supervised learning","code":"https://github.com/lmcinnes/umap","n_code_links":2,"syntology":{"n_ran":4,"n_unverified":0,"n_samples":4,"n_pointer_only_licence":0}},{"rank_in_archive_order":4,"model":"UMAP","metrics":{"Classification Accuracy":"41.3"},"uses_additional_data":false,"paper_date":"2018-02-09","paper":"/paper/umap-uniform-manifold-approximation-and","paper_url":"https://arxiv.org/abs/1802.03426v3","paper_title":"UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction","code":"https://github.com/lmcinnes/umap","n_code_links":39,"syntology":null}],"since_archive":{"present":false,"note":"No Syntology-extracted rows are published in this build."},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per row: N of M harvested code samples from that row's paper executed on a synthesized fixture; the other M-N are unverified. Not a reproduction of the row's number; not a correctness claim. n_pointer_only_licence counts samples the site points at rather than redistributes (a licence axis, independent of ran/unverified).","rows_with_graph_line":1,"rows_with_any_sample_ran":1,"distinct_papers_with_graph_line":1,"distinct_papers_with_any_sample_ran":1,"samples_over_distinct_papers":{"n_ran":4,"n_unverified":0,"n_samples":4,"n_pointer_only_licence":0,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":4,"n_unverified":0,"n_samples":4,"n_pointer_only_licence":0,"note":"row-weighted: a paper behind several rows is counted once per row; inflated relative to samples_over_distinct_papers by design, kept for readers summing the per-row syntology blocks"}}}