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pro vyhledávání: '"Yu E Lin"'
Publikováno v:
Applied Intelligence. 53:12873-12887
Publikováno v:
Multimedia Systems. 28:1059-1067
Despite Siamese-based trackers have achieved great success in recent years, researchers have focused more on the accuracy of trackers than their complexity, which leads to their inapplicability in some scenarios, and the real-time speed can be greatl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::22cd7c4907b79c99609bb3eddc764c6e
https://doi.org/10.21203/rs.3.rs-2201616/v1
https://doi.org/10.21203/rs.3.rs-2201616/v1
Autor:
Yu E Lin, Yong Cun Guo
Publikováno v:
Applied Mechanics and Materials. :54-57
Unsupervised Discriminant Projection (UDP) is a typical manifold-based dimensionality reduction method, and has been successfully applied in face recognition. However, UDP suffers from the small sample size problem and usually deteriorates because th
Autor:
Yu E Lin, Xing Zhu Liang
Publikováno v:
Advanced Materials Research. :2551-2554
In recent years, a variety of manifold-based learning dimensionality reduction techniques have been proposed, which attempt to project the original data into a lower dimensional feature space by preserving the local neighborhood structure. Among them
Publikováno v:
Advanced Materials Research. :1343-1346
Unsupervised Discriminant Projection (UDP) is one of the most promising feature extraction methods. However, UDP suffers from the small sample size problem and the optimal basis vectors obtained by the UDP are nonorthogonal. In this paper, we present
Publikováno v:
Advanced Materials Research. 339:571-574
Several orthogonal feature extraction algorithms based on local preserving projection have recently been proposed. However, these methods still are linear techniques in nature. In this paper, we present nonlinear feature extraction method called Kern
Publikováno v:
MATEC Web of Conferences, Vol 61, p 02012 (2016)
Feature extraction is a crucial step for face recognition. In this paper, based on supervised local structure and diversity projection (SLSDP), a new feature extraction method called orthogonal discriminant diversity and similarity preserving project