{"id":"W2949803069","doi":"10.48550/arxiv.1704.02345","title":"Fast Spectral Clustering Using Autoencoders and Landmarks","year":2017,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Spectral clustering; Adjacency matrix; Cluster analysis; Laplacian matrix; Autoencoder; Computer science; Computational complexity theory; Graph; Adjacency list; Matrix decomposition; Pattern recognition (psychology); Spectral graph theory; Eigenvalues and eigenvectors; Artificial intelligence; Algorithm; Matrix (chemical analysis); Theoretical computer science; Deep learning","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":[0.001108911,0.001491918,0.001594969,0.001999967,0.0008497491,0.001200063,0.002026079,0.001309122,0.002288834],"category_scores_gemma":[0.003627649,0.0009425248,0.00119398,0.001829848,0.0008707191,0.002018979,0.001849398,0.0019511,0.002223206],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001104755,"about_ca_system_score_gemma":0.0014674,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007918641,"about_ca_topic_score_gemma":0.01250048,"domain_scores_codex":[0.9988894,0.000197642,0.00005763149,0.0003889089,0.0003375549,0.000128856],"domain_scores_gemma":[0.9986186,0.0004147073,0.0001309443,0.0003572968,0.0004090534,0.00006937046],"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":[0.000243944,0.0001854252,0.001662127,0.0001237675,0.0001734105,0.0001055411,0.0001941575,0.4100117,0.01676544,0.0160658,0.006892614,0.5475761],"study_design_scores_gemma":[0.000006591992,0.00001704205,0.0002555322,0.00000720239,0.000006752315,0.0000292433,0.0000247193,0.9885696,0.003264699,0.006879983,0.0009274041,0.00001119247],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007841452,0.0001181528,0.9894364,0.00006658864,0.00003502058,0.00002955499,0.00006748334,0.001901881,0.0005034653],"genre_scores_gemma":[0.1584827,0.0002158563,0.8368425,0.0001471564,0.00006554259,0.0001713443,0.0009359434,0.000461842,0.002677174],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007918641,"threshold_uncertainty_score":0.0157451,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09574453108513345,"score_gpt":0.2059066529691377,"score_spread":0.1101621218840042,"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."}}