FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2915691082> ?p ?o ?g. }

• W2915691082 endingPage "1728" @default.
• W2915691082 startingPage "1713" @default.
• W2915691082 abstract "Many vision-based applications rely on logistic regression for embedding classification within a probabilistic context, such as recognition in images and videos or identifying disease-specific image phenotypes from neuroimages. Logistic regression, however, often performs poorly when trained on data that is noisy, has irrelevant features, or when the samples are distributed across the classes in an imbalanced setting; a common occurrence in visual recognition tasks. To deal with those issues, researchers generally rely on adhoc regularization techniques or model a subset of these issues. We instead propose a mathematically sound logistic regression model that selects a subset of (relevant) features and (informative and balanced) set of samples during the training process. The model does so by applying cardinality constraints (via ℓ <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>0</sub> -`norm' sparsity) on the features and samples. ℓ <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>0</sub> defines sparsity in mathematical settings but in practice has mostly been approximated (e.g., via ℓ <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>1</sub> or its variations) for computational simplicity. We prove that a local minimum to the non-convex optimization problems induced by cardinality constraints can be computed by combining block coordinate descent with penalty decomposition. On synthetic, image recognition, and neuroimaging datasets, we show that the accuracy of the method is higher than alternative methods and classifiers commonly used in the literature." @default.
• W2915691082 created "2019-03-02" @default.
• W2915691082 creator A5015355317 @default.
• W2915691082 creator A5023179237 @default.
• W2915691082 creator A5055107125 @default.
• W2915691082 creator A5060412479 @default.
• W2915691082 creator A5085075728 @default.
• W2915691082 date "2020-07-01" @default.
• W2915691082 modified "2023-10-14" @default.
• W2915691082 title "Logistic Regression Confined by Cardinality-Constrained Sample and Feature Selection" @default.
• W2915691082 cites W1550998176 @default.
• W2915691082 cites W1964290367 @default.
• W2915691082 cites W1980454827 @default.
• W2915691082 cites W1988056000 @default.
• W2915691082 cites W1990653740 @default.
• W2915691082 cites W1996516822 @default.
• W2915691082 cites W1997201895 @default.
• W2915691082 cites W1999318832 @default.
• W2915691082 cites W2006793117 @default.
• W2915691082 cites W2008258216 @default.
• W2915691082 cites W2008339309 @default.
• W2915691082 cites W2012961725 @default.
• W2915691082 cites W2015866962 @default.
• W2915691082 cites W2017026587 @default.
• W2915691082 cites W2020214980 @default.
• W2915691082 cites W2024991751 @default.
• W2915691082 cites W2028384450 @default.
• W2915691082 cites W2028781966 @default.
• W2915691082 cites W2032365301 @default.
• W2915691082 cites W2034460838 @default.
• W2915691082 cites W2036439084 @default.
• W2915691082 cites W2037837401 @default.
• W2915691082 cites W2047885427 @default.
• W2915691082 cites W2069671092 @default.
• W2915691082 cites W2074682976 @default.
• W2915691082 cites W2076807660 @default.
• W2915691082 cites W2078381696 @default.
• W2915691082 cites W2085261163 @default.
• W2915691082 cites W2085805928 @default.
• W2915691082 cites W2087578495 @default.
• W2915691082 cites W2088150542 @default.
• W2915691082 cites W2097390792 @default.
• W2915691082 cites W2107861471 @default.
• W2915691082 cites W2111446187 @default.
• W2915691082 cites W2112796928 @default.
• W2915691082 cites W2116148865 @default.
• W2915691082 cites W2117553576 @default.
• W2915691082 cites W2118978333 @default.
• W2915691082 cites W2119387367 @default.
• W2915691082 cites W2119862467 @default.
• W2915691082 cites W2122504634 @default.
• W2915691082 cites W2122825543 @default.
• W2915691082 cites W2126607811 @default.
• W2915691082 cites W2134732827 @default.
• W2915691082 cites W2135760566 @default.
• W2915691082 cites W2137130182 @default.
• W2915691082 cites W2142592339 @default.
• W2915691082 cites W2142727674 @default.
• W2915691082 cites W2144182447 @default.
• W2915691082 cites W2147898188 @default.
• W2915691082 cites W2148102854 @default.
• W2915691082 cites W2148143831 @default.
• W2915691082 cites W2154053567 @default.
• W2915691082 cites W2160323946 @default.
• W2915691082 cites W2189265340 @default.
• W2915691082 cites W2204904589 @default.
• W2915691082 cites W236122260 @default.
• W2915691082 cites W2394982463 @default.
• W2915691082 cites W2412188599 @default.
• W2915691082 cites W2489822048 @default.
• W2915691082 cites W2498119267 @default.
• W2915691082 cites W2502224699 @default.
• W2915691082 cites W2531607313 @default.
• W2915691082 cites W2541820485 @default.
• W2915691082 cites W2582559660 @default.
• W2915691082 cites W2583826029 @default.
• W2915691082 cites W2776542183 @default.
• W2915691082 cites W2784313390 @default.
• W2915691082 cites W2963061824 @default.
• W2915691082 cites W2963579802 @default.
• W2915691082 cites W2963598490 @default.
• W2915691082 cites W323802126 @default.
• W2915691082 cites W4229873072 @default.
• W2915691082 cites W4234173777 @default.
• W2915691082 cites W4244883719 @default.
• W2915691082 doi "https://doi.org/10.1109/tpami.2019.2901688" @default.
• W2915691082 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7331794" @default.
• W2915691082 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30835210" @default.
• W2915691082 hasPublicationYear "2020" @default.
• W2915691082 type Work @default.
• W2915691082 sameAs 2915691082 @default.
• W2915691082 citedByCount "22" @default.
• W2915691082 countsByYear W29156910822019 @default.
• W2915691082 countsByYear W29156910822020 @default.
• W2915691082 countsByYear W29156910822021 @default.
• W2915691082 countsByYear W29156910822022 @default.
• W2915691082 countsByYear W29156910822023 @default.
• W2915691082 crossrefType "journal-article" @default.

• next