{"id":"W3131860561","doi":"10.1016/j.cosrev.2021.100378","title":"Conceptual and empirical comparison of dimensionality reduction algorithms (PCA, KPCA, LDA, MDS, SVD, LLE, ISOMAP, LE, ICA, t-SNE)","year":2021,"lang":"en","type":"article","venue":"Computer Science Review","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":824,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ericsson (Canada); University of Regina","funders":"","keywords":"Isomap; Dimensionality reduction; Computer science; Pattern recognition (psychology); Artificial intelligence; Nonlinear dimensionality reduction; Random projection; Feature extraction; Projection pursuit; Projection (relational algebra); Curse of dimensionality; Principal component analysis; Algorithm; Machine learning","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07235552708127922,"score_gpt":0.358271541727599,"score_spread":0.2859160146463197,"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."}}