Zobrazeno 1 - 10
of 170
pro vyhledávání: '"Minghua Wan"'
Autor:
Yu YAO, Minghua WAN
Publikováno v:
智能科学与技术学报, Vol 3, Pp 342-350 (2021)
Non-negative matrix factorization (NMF) has been widely used.However, NMF pays more attention to the local information of the data, it ignores the global representation of the data.In terms of image classification, the global information of the data
Externí odkaz:
https://doaj.org/article/052fdce9c2fa405393591bef1f15890c
Publikováno v:
Mathematics, Vol 11, Iss 7, p 1722 (2023)
The two-dimensional discriminant locally preserved projections (2DDLPP) algorithm adds a between-class weighted matrix and a within-class weighted matrix into the objective function of the two-dimensional locally preserved projections (2DLPP) algorit
Externí odkaz:
https://doaj.org/article/ea8bddbf2909406f9272a01db9c2cb5c
Publikováno v:
Mathematics, Vol 11, Iss 7, p 1716 (2023)
Graph regularized non-negative matrix factorization (GNMF) is widely used in feature extraction. In the process of dimensionality reduction, GNMF can retain the internal manifold structure of data by adding a regularizer to non-negative matrix factor
Externí odkaz:
https://doaj.org/article/6657489b04274e57b362fc6fe96e1104
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 377-388 (2021)
Change detection (CD) is a hot issue in the research of remote sensing technology. Hyperspectral images (HSIs) greatly promote the development of CD technology because of their high resolution in the spectral domain. However, some traditional CD meth
Externí odkaz:
https://doaj.org/article/ce48051041944536bbd08e1a48cbbbf5
Autor:
Guowei Yang, Shaohua Qi, Teng Yu, Minghua Wan, Zhangjing Yang, Tianming Zhan, Fanlong Zhang, Zhihui Lai
Publikováno v:
IEEE Access, Vol 8, Pp 47914-47924 (2020)
At present, regions of the same class determined by Support Vector Machines (SVM) classifier, Support Vector Domain Description (SVDD) classifier and Deep Learning (DL) classifier may occupy regions of other classes or unknown classes in feature spac
Externí odkaz:
https://doaj.org/article/ed9e3fc7d4904d14986c1016897c7b55
Publikováno v:
Mathematics, Vol 10, Iss 24, p 4725 (2022)
In order to solve the problem that the F1-measure value and the AUROC value of some classical open-set classifier methods do not exceed 40% in high-openness scenarios, this paper proposes an algorithm combining negative-class feature enhancement lear
Externí odkaz:
https://doaj.org/article/24e8d0df85e641fdabf5a659a51e0ffc
Publikováno v:
Mathematics, Vol 10, Iss 23, p 4603 (2022)
The main idea of principal component analysis (PCA) is to transform the problem of high-dimensional space into low-dimensional space, and obtain the output sample set after a series of operations on the samples. However, the accuracy of the tradition
Externí odkaz:
https://doaj.org/article/7892e96b320d407689660558c77a569c
Publikováno v:
Mathematics, Vol 10, Iss 22, p 4314 (2022)
Non-negative matrix factorization (NMF) is a fundamental theory that has received much attention and is widely used in image engineering, pattern recognition and other fields. However, the classical NMF has limitations such as only focusing on local
Externí odkaz:
https://doaj.org/article/e248a959b71c412582cc40d24f58bb3d
Publikováno v:
IEEE Access, Vol 7, Pp 11868-11881 (2019)
Recently, regression-based classifiers, such as the sparse representation classifier and collaborative representation classifier, have been proposed for hyperspectral image (HSI) classification. However, HSIs are typically corrupted by noise, occlusi
Externí odkaz:
https://doaj.org/article/09efc95f52ff4282ad1c082aece66945
Publikováno v:
IEEE Access, Vol 7, Pp 114714-114720 (2019)
A new image feature extraction method for face recognition called Tri-direction 2D-Fisher Discriminant Analysis (T2D-FDA) is proposed to deal with the Small Sample Size (SSS) problem in conventional 1D-Fisher Discriminant Analysis (1D-FDA). Moreover,
Externí odkaz:
https://doaj.org/article/ff5f24fa13ef41ae889b7a0143255491