Matches in SemOpenAlex for { <https://semopenalex.org/work/W3204652201> ?p ?o ?g. }
Showing items 1 to 92 of
92
with 100 items per page.
- W3204652201 endingPage "592" @default.
- W3204652201 startingPage "573" @default.
- W3204652201 abstract "• Cancer survivability prediction is a significant problem to health professionals. • A novel classification algorithm is proposed using low-rank and sparse representation . • Low-rank alternative of raw inputs is trained using a sparsity-enhanced classifier . • Experiments show superior performance compared to state-of-the-art approaches. Cancer survivability prediction has been of great interest to health professionals and researchers. The task refers to the procedure of estimating the potential survivability according to an individual’s medical history. The difficulty is that raw data is usually subject to some noise, such as missing values. To address this issue, we propose a novel low-rank and sparse representation-based learning algorithm, which consists of two main stages of data self expressiveness and classification. Firstly, in the data self expressiveness stage, raw inputs have been decomposed into one dictionary (which is enforced with a low-rank constraint) and one coefficient matrix (which is sparsely coded), respectively. Secondly, this sparse coefficient matrix is paired with sample labels for training during the classification stage. We further integrate these two stages and formulate them into an optimization problem, which is then solved using an iterative computational strategy. Theoretically, we analyze the convergence of the proposed algorithm. The relationship between the proposed algorithm and existing approaches are also discussed. The efficiency of the proposed algorithm is experimentally verified using several benchmarking classification problems and a public longitudinal dataset. Experimental results demonstrate that the proposed algorithm achieves superior performance in terms of affordable computational complexity and high prediction accuracy, compared to state-of-the-art approaches." @default.
- W3204652201 created "2021-10-11" @default.
- W3204652201 creator A5007036605 @default.
- W3204652201 creator A5014462090 @default.
- W3204652201 creator A5015817857 @default.
- W3204652201 creator A5020663450 @default.
- W3204652201 creator A5071696276 @default.
- W3204652201 date "2022-01-01" @default.
- W3204652201 modified "2023-10-10" @default.
- W3204652201 title "Low-rank and sparse representation based learning for cancer survivability prediction" @default.
- W3204652201 cites W1963932623 @default.
- W3204652201 cites W1973775644 @default.
- W3204652201 cites W1983024255 @default.
- W3204652201 cites W1989628460 @default.
- W3204652201 cites W2021770241 @default.
- W3204652201 cites W2071631699 @default.
- W3204652201 cites W2115429828 @default.
- W3204652201 cites W2126607811 @default.
- W3204652201 cites W2129812935 @default.
- W3204652201 cites W2145962650 @default.
- W3204652201 cites W2164625711 @default.
- W3204652201 cites W2238296078 @default.
- W3204652201 cites W2500665848 @default.
- W3204652201 cites W2530755795 @default.
- W3204652201 cites W2756063524 @default.
- W3204652201 cites W2773328008 @default.
- W3204652201 cites W2884277568 @default.
- W3204652201 cites W2890937966 @default.
- W3204652201 cites W2916995168 @default.
- W3204652201 cites W2986609444 @default.
- W3204652201 cites W3012669251 @default.
- W3204652201 cites W3034145398 @default.
- W3204652201 cites W3099438410 @default.
- W3204652201 doi "https://doi.org/10.1016/j.ins.2021.10.013" @default.
- W3204652201 hasPublicationYear "2022" @default.
- W3204652201 type Work @default.
- W3204652201 sameAs 3204652201 @default.
- W3204652201 citedByCount "7" @default.
- W3204652201 countsByYear W32046522012022 @default.
- W3204652201 countsByYear W32046522012023 @default.
- W3204652201 crossrefType "journal-article" @default.
- W3204652201 hasAuthorship W3204652201A5007036605 @default.
- W3204652201 hasAuthorship W3204652201A5014462090 @default.
- W3204652201 hasAuthorship W3204652201A5015817857 @default.
- W3204652201 hasAuthorship W3204652201A5020663450 @default.
- W3204652201 hasAuthorship W3204652201A5071696276 @default.
- W3204652201 hasConcept C11413529 @default.
- W3204652201 hasConcept C114614502 @default.
- W3204652201 hasConcept C119857082 @default.
- W3204652201 hasConcept C124066611 @default.
- W3204652201 hasConcept C124101348 @default.
- W3204652201 hasConcept C153180895 @default.
- W3204652201 hasConcept C154945302 @default.
- W3204652201 hasConcept C164226766 @default.
- W3204652201 hasConcept C2781133158 @default.
- W3204652201 hasConcept C31258907 @default.
- W3204652201 hasConcept C33923547 @default.
- W3204652201 hasConcept C41008148 @default.
- W3204652201 hasConcept C95623464 @default.
- W3204652201 hasConceptScore W3204652201C11413529 @default.
- W3204652201 hasConceptScore W3204652201C114614502 @default.
- W3204652201 hasConceptScore W3204652201C119857082 @default.
- W3204652201 hasConceptScore W3204652201C124066611 @default.
- W3204652201 hasConceptScore W3204652201C124101348 @default.
- W3204652201 hasConceptScore W3204652201C153180895 @default.
- W3204652201 hasConceptScore W3204652201C154945302 @default.
- W3204652201 hasConceptScore W3204652201C164226766 @default.
- W3204652201 hasConceptScore W3204652201C2781133158 @default.
- W3204652201 hasConceptScore W3204652201C31258907 @default.
- W3204652201 hasConceptScore W3204652201C33923547 @default.
- W3204652201 hasConceptScore W3204652201C41008148 @default.
- W3204652201 hasConceptScore W3204652201C95623464 @default.
- W3204652201 hasLocation W32046522011 @default.
- W3204652201 hasOpenAccess W3204652201 @default.
- W3204652201 hasPrimaryLocation W32046522011 @default.
- W3204652201 hasRelatedWork W2001652754 @default.
- W3204652201 hasRelatedWork W2379065761 @default.
- W3204652201 hasRelatedWork W2549006548 @default.
- W3204652201 hasRelatedWork W2563096758 @default.
- W3204652201 hasRelatedWork W2623427976 @default.
- W3204652201 hasRelatedWork W2807311372 @default.
- W3204652201 hasRelatedWork W2961085424 @default.
- W3204652201 hasRelatedWork W2990531703 @default.
- W3204652201 hasRelatedWork W3043252291 @default.
- W3204652201 hasRelatedWork W4214932115 @default.
- W3204652201 hasVolume "582" @default.
- W3204652201 isParatext "false" @default.
- W3204652201 isRetracted "false" @default.
- W3204652201 magId "3204652201" @default.
- W3204652201 workType "article" @default.