{"id":"W2951706336","doi":"10.48550/arxiv.1306.2663","title":"Large Margin Low Rank Tensor Analysis","year":2013,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Tensor decomposition and applications","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Tensor (intrinsic definition); Generalization; Computer science; Artificial intelligence; Nonlinear dimensionality reduction; Manifold (fluid mechanics); Margin (machine learning); Pattern recognition (psychology); Curse of dimensionality; Dimensionality reduction; Similarity (geometry); Rank (graph theory); Euclidean space; Object (grammar); Cognition; Feature vector; Mathematics; Machine learning; Pure mathematics; Image (mathematics); Psychology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002435349,0.001960915,0.001559081,0.00119709,0.0006819285,0.001868805,0.001303179,0.001353212,0.003913425],"category_scores_gemma":[0.007907973,0.0004817909,0.0009244283,0.001256536,0.001557455,0.002122459,0.001584784,0.002512709,0.002616276],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000959495,"about_ca_system_score_gemma":0.001557705,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002815414,"about_ca_topic_score_gemma":0.002801781,"domain_scores_codex":[0.9986463,0.0005872062,0.00006904133,0.0003006645,0.0002971446,0.00009966576],"domain_scores_gemma":[0.9971136,0.00104102,0.0004201729,0.0007613233,0.0005292133,0.0001346457],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002918606,0.0001649104,0.00188404,0.0005347372,0.0002271632,0.0002044767,0.0001679841,0.4677905,0.009068358,0.1534097,0.03535741,0.3308989],"study_design_scores_gemma":[0.000007590925,0.00003534018,0.0002673542,0.00001352517,0.000009123785,0.00003251286,0.00001528532,0.9331396,0.0009971391,0.0631782,0.002291168,0.00001321585],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00785923,0.000657899,0.9880056,0.0007340822,0.00006266169,0.0000357516,0.0002833153,0.0008821622,0.001479382],"genre_scores_gemma":[0.5317197,0.002567096,0.4448965,0.0005982639,0.0007458064,0.0003133153,0.003785087,0.000528536,0.01484568],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003913425,"threshold_uncertainty_score":0.01309174,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08061920354587528,"score_gpt":0.2293455989999257,"score_spread":0.1487263954540504,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}