{"id":"W1978771480","doi":"10.1162/neco_a_00570","title":"Large Margin Low Rank Tensor Analysis","year":2014,"lang":"en","type":"article","venue":"Neural Computation","topic":"Tensor decomposition and applications","field":"Mathematics","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"Luonnontieteiden ja Tekniikan Tutkimuksen Toimikunta; Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Dimensionality reduction; Tensor (intrinsic definition); Margin (machine learning); Curse of dimensionality; Pattern recognition (psychology); Mathematics; Projection (relational algebra); Artificial intelligence; Data point; Rank (graph theory); Computer science; Representation (politics); Dimension (graph theory); Algorithm; Machine learning; Combinatorics; Pure mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001504763,0.00009600305,0.0001800657,0.0001435983,0.0001294564,0.00004686392,0.00008609727,0.00004053117,0.0001227903],"category_scores_gemma":[0.00005329738,0.00008925654,0.0001297324,0.0004805525,0.00001467851,0.00006843243,0.00001879276,0.00008264965,0.000125703],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001714292,"about_ca_system_score_gemma":0.000003565608,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003238533,"about_ca_topic_score_gemma":0.00001193538,"domain_scores_codex":[0.9991809,0.00009279649,0.000224145,0.0001925738,0.000163131,0.0001464306],"domain_scores_gemma":[0.9993184,0.0002486298,0.0001057388,0.0001742372,0.00009589543,0.00005713606],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001589574,0.002168835,0.01786225,0.0003502305,0.001159466,0.0000132891,0.002779447,0.05841105,0.01238979,0.7916988,0.05920086,0.05380699],"study_design_scores_gemma":[0.0007041632,0.00003304019,0.03558354,0.000006056095,0.000272116,0.000004674659,0.00004421098,0.9019736,0.0003336434,0.05915928,0.001702193,0.000183494],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5752074,0.000003073487,0.4223323,0.0008370276,0.00004501658,0.000137547,0.00001082555,0.0001765238,0.001250205],"genre_scores_gemma":[0.988945,6.011603e-7,0.0101277,0.0004705519,0.00007269672,0.000018113,0.0000937624,0.00001271898,0.0002588429],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8435625,"threshold_uncertainty_score":0.3639776,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02700694190340837,"score_gpt":0.3218331844378574,"score_spread":0.2948262425344491,"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."}}