{"paper":{"title":"Efficient Parametric Projection Pursuit Density Estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Geoffrey E. Hinton, Max Welling, Richard S. Zemel","submitted_at":"2012-10-19T15:08:28Z","abstract_excerpt":"Product models of low dimensional experts are a powerful way to avoid the     curse of dimensionality. We present the ``under-complete product of experts'     (UPoE), where each expert models a one dimensional projection of the data. The     UPoE is fully tractable and may be interpreted as a parametric probabilistic     model for projection pursuit. Its ML learning rules are identical to the     approximate learning rules proposed before for under-complete ICA. We also     derive an efficient sequential learning algorithm and discuss its relationship     to projection pursuit density estimati"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1212.2513","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}