Cargando…
Dimensionality Reduction by Supervised Neighbor Embedding Using Laplacian Search
Dimensionality reduction is an important issue for numerous applications including biomedical images analysis and living system analysis. Neighbor embedding, those representing the global and local structure as well as dealing with multiple manifolds, such as the elastic embedding techniques, can go...
Autores principales: | Zheng, Jianwei, Zhang, Hangke, Cattani, Carlo, Wang, Wanliang |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2014
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4055433/ https://www.ncbi.nlm.nih.gov/pubmed/24963339 http://dx.doi.org/10.1155/2014/594379 |
Ejemplares similares
-
Fast Discriminative Stochastic Neighbor Embedding Analysis
por: Zheng, Jianwei, et al.
Publicado: (2013) -
Kinase Identification with Supervised Laplacian Regularized Least Squares
por: Li, Ao, et al.
Publicado: (2015) -
Dimensionality reduction with unsupervised nearest neighbors
por: Kramer, Oliver
Publicado: (2013) -
Time-Series Laplacian Semi-Supervised Learning for Indoor Localization †
por: Yoo, Jaehyun
Publicado: (2019) -
Efficient embedding of complex networks to hyperbolic space via their Laplacian
por: Alanis-Lobato, Gregorio, et al.
Publicado: (2016)