Matches in SemOpenAlex for { <https://semopenalex.org/work/W2996075303> ?p ?o ?g. }
- W2996075303 endingPage "4697" @default.
- W2996075303 startingPage "4690" @default.
- W2996075303 abstract "Locality preserving projection (LPP) has been often used as a dimensionality reduction tool for hyperspectral image analysis especially in the context of classification since it provides a projection matrix for embedding test samples to low dimensional space. However, the performance of LPP heavily depends on the optimization of two parameters of the graph affinity matrix: k-nearest neighbor and heat kernel width, when one considers an isotropic kernel. These two parameters might be optimally chosen simply based on a grid search. In case of using a generalized heat kernel where each feature is separately weighted by a kernel width, the number of parameters that need to be optimized is related to the number of features of the dataset, which might not be very easy to tune. Therefore, in this article, we propose to use heuristic methods, including genetic algorithm (GA), harmony search (HS), and particle swarm optimization (PSO), to explore the effects of the heat kernel parameters aiming to analyze the embedding quality of LPP's projection in terms of various aspects, including 1-NN classification accuracy, locality preserving power, and quality of the graph affinity matrix. The results obtained with the experiments on three hyperspectral datasets show that HS performs better than GA and PSO in optimizing the parameters of the affinity matrix, and the generalized heat kernel achieves better performance than the isotropic kernel. Additionally, a feature selection application is performed by using the kernel width of the generalized heat kernel for each heuristic method. The results show that very promising results are obtained in comparison with the state-of-the-art feature selection methods." @default.
- W2996075303 created "2019-12-26" @default.
- W2996075303 creator A5002263525 @default.
- W2996075303 creator A5058660643 @default.
- W2996075303 date "2019-12-01" @default.
- W2996075303 modified "2023-09-23" @default.
- W2996075303 title "An Adaptive Affinity Matrix Optimization for Locality Preserving Projection via Heuristic Methods for Hyperspectral Image Analysis" @default.
- W2996075303 cites W1978775764 @default.
- W2996075303 cites W1981791883 @default.
- W2996075303 cites W1993885071 @default.
- W2996075303 cites W2001141328 @default.
- W2996075303 cites W2005106632 @default.
- W2996075303 cites W2052214166 @default.
- W2996075303 cites W2070127246 @default.
- W2996075303 cites W2081749411 @default.
- W2996075303 cites W2088358970 @default.
- W2996075303 cites W2090826137 @default.
- W2996075303 cites W2095392975 @default.
- W2996075303 cites W2097308346 @default.
- W2996075303 cites W2104294146 @default.
- W2996075303 cites W2111427896 @default.
- W2996075303 cites W2118439480 @default.
- W2996075303 cites W2123961273 @default.
- W2996075303 cites W2126029357 @default.
- W2996075303 cites W2132914434 @default.
- W2996075303 cites W2135996040 @default.
- W2996075303 cites W2148633389 @default.
- W2996075303 cites W2149544245 @default.
- W2996075303 cites W2152195021 @default.
- W2996075303 cites W2155985040 @default.
- W2996075303 cites W2405835588 @default.
- W2996075303 cites W2592026470 @default.
- W2996075303 cites W2598351993 @default.
- W2996075303 cites W2604137995 @default.
- W2996075303 cites W2730849903 @default.
- W2996075303 cites W2735797020 @default.
- W2996075303 cites W2738091946 @default.
- W2996075303 cites W2792584708 @default.
- W2996075303 cites W2810170362 @default.
- W2996075303 cites W2894458221 @default.
- W2996075303 cites W2895909348 @default.
- W2996075303 cites W2945595726 @default.
- W2996075303 cites W3105250825 @default.
- W2996075303 cites W3148981562 @default.
- W2996075303 cites W4250503569 @default.
- W2996075303 cites W84941271 @default.
- W2996075303 doi "https://doi.org/10.1109/jstars.2019.2947355" @default.
- W2996075303 hasPublicationYear "2019" @default.
- W2996075303 type Work @default.
- W2996075303 sameAs 2996075303 @default.
- W2996075303 citedByCount "4" @default.
- W2996075303 countsByYear W29960753032021 @default.
- W2996075303 countsByYear W29960753032022 @default.
- W2996075303 crossrefType "journal-article" @default.
- W2996075303 hasAuthorship W2996075303A5002263525 @default.
- W2996075303 hasAuthorship W2996075303A5058660643 @default.
- W2996075303 hasConcept C11413529 @default.
- W2996075303 hasConcept C114614502 @default.
- W2996075303 hasConcept C122280245 @default.
- W2996075303 hasConcept C12267149 @default.
- W2996075303 hasConcept C126255220 @default.
- W2996075303 hasConcept C153180895 @default.
- W2996075303 hasConcept C154945302 @default.
- W2996075303 hasConcept C159078339 @default.
- W2996075303 hasConcept C33923547 @default.
- W2996075303 hasConcept C41008148 @default.
- W2996075303 hasConcept C70518039 @default.
- W2996075303 hasConcept C74193536 @default.
- W2996075303 hasConcept C85617194 @default.
- W2996075303 hasConceptScore W2996075303C11413529 @default.
- W2996075303 hasConceptScore W2996075303C114614502 @default.
- W2996075303 hasConceptScore W2996075303C122280245 @default.
- W2996075303 hasConceptScore W2996075303C12267149 @default.
- W2996075303 hasConceptScore W2996075303C126255220 @default.
- W2996075303 hasConceptScore W2996075303C153180895 @default.
- W2996075303 hasConceptScore W2996075303C154945302 @default.
- W2996075303 hasConceptScore W2996075303C159078339 @default.
- W2996075303 hasConceptScore W2996075303C33923547 @default.
- W2996075303 hasConceptScore W2996075303C41008148 @default.
- W2996075303 hasConceptScore W2996075303C70518039 @default.
- W2996075303 hasConceptScore W2996075303C74193536 @default.
- W2996075303 hasConceptScore W2996075303C85617194 @default.
- W2996075303 hasFunder F4320322626 @default.
- W2996075303 hasIssue "12" @default.
- W2996075303 hasLocation W29960753031 @default.
- W2996075303 hasOpenAccess W2996075303 @default.
- W2996075303 hasPrimaryLocation W29960753031 @default.
- W2996075303 hasRelatedWork W1562318760 @default.
- W2996075303 hasRelatedWork W2040848081 @default.
- W2996075303 hasRelatedWork W2068555361 @default.
- W2996075303 hasRelatedWork W2068942791 @default.
- W2996075303 hasRelatedWork W2110459882 @default.
- W2996075303 hasRelatedWork W2125244435 @default.
- W2996075303 hasRelatedWork W2145759202 @default.
- W2996075303 hasRelatedWork W2168272488 @default.
- W2996075303 hasRelatedWork W3154145980 @default.
- W2996075303 hasRelatedWork W43316407 @default.
- W2996075303 hasVolume "12" @default.