{"id":"W2593429156","doi":"10.1007/978-3-319-54181-5_22","title":"SSP: Supervised Sparse Projections for Large-Scale Retrieval in High Dimensions","year":2017,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Leverage (statistics); Artificial intelligence; Encoding (memory); Convolutional neural network; Pattern recognition (psychology); Binary number; Projection (relational algebra); RGB color model; Neural coding; Binary code; Sparse matrix; Algorithm","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0009703781,0.001600867,0.001718701,0.0008253177,0.0005299768,0.001341389,0.00189091,0.001531018,0.008025671],"category_scores_gemma":[0.003548322,0.0007435792,0.001019726,0.002056515,0.0009215904,0.002386471,0.003217668,0.002508814,0.006260654],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000323732,"about_ca_system_score_gemma":0.0009329253,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002035276,"about_ca_topic_score_gemma":0.002893146,"domain_scores_codex":[0.9990026,0.0002543242,0.00004976589,0.0001780775,0.0004431079,0.00007209213],"domain_scores_gemma":[0.9986766,0.000534254,0.00007505048,0.0003790794,0.0002636097,0.00007143646],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003246664,0.0002068098,0.0002980144,0.0004005379,0.0001275023,0.0001507429,0.0001302401,0.07666363,0.02299705,0.02186059,0.06027505,0.8165652],"study_design_scores_gemma":[0.00004206702,0.00009427738,0.0002018091,0.00001727443,0.00001732158,0.0001727747,0.00003404765,0.9571185,0.007358969,0.02716731,0.007750237,0.00002552153],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001763175,0.0002256386,0.9949186,0.00009208751,0.00006179144,0.00003964168,0.0002037398,0.002094314,0.0006008965],"genre_scores_gemma":[0.05117335,0.0006243133,0.9374087,0.000214629,0.0002371593,0.0003257422,0.002198747,0.0007623764,0.007054908],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008025671,"threshold_uncertainty_score":0.02684855,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02869503918154223,"score_gpt":0.295643243122963,"score_spread":0.2669482039414207,"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."}}