Zobrazeno 1 - 10
of 11
pro vyhledávání: '"Qingjiang Xiao"'
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 18987-19002 (2024)
In recent years, tensor representation-based approaches have been widely studied in hyperspectral anomaly detection. However, these methods still suffer from two key issues. First, the various complex regularizations imposed on the background compone
Externí odkaz:
https://doaj.org/article/8a580092959943b3ba22338478229b32
Publikováno v:
IEEE Access, Vol 9, Pp 118851-118860 (2021)
In this paper, we present a hyper-Laplacian regularized method WHLR-MSC with a new weighted tensor nuclear norm for multi-view subspace clustering. Specifically, we firstly stack the subspace representation matrices of the different views into a tens
Externí odkaz:
https://doaj.org/article/7925748b60274769a63064479b95f29f
Multi-view spectral clustering based on adaptive neighbor learning and low-rank tensor decomposition
Publikováno v:
Multimedia Tools and Applications.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5f00a1061988c5d71e9962c70d322632
https://doi.org/10.1007/s11042-023-15018-4
https://doi.org/10.1007/s11042-023-15018-4
Publikováno v:
Journal of Intelligent & Fuzzy Systems. 42:5809-5822
In recent years, tensor-Singular Value Decomposition (t-SVD) based tensor nuclear norm has achieved remarkable progress in multi-view subspace clustering. However, most existing clustering methods still have the following shortcomings: (a) It has no
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f822f83f5cb3fb961e8544ee53bc2a08
https://doi.org/10.3233/jifs-212316
https://doi.org/10.3233/jifs-212316
Publikováno v:
Neurocomputing. 458:204-218
Low-rank tensor completion (LRTC) has gained significant attention due to its powerful capability of recovering missing entries. However, it has to repeatedly calculate the time-consuming singular value decomposition (SVD). To address this drawback,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8d2e715fab7b45798d8ecce9bb5fe54a
https://doi.org/10.1016/j.neucom.2021.06.020
https://doi.org/10.1016/j.neucom.2021.06.020
Publikováno v:
Neural Processing Letters. 54:265-283
The multi-view algorithm based on graph learning pays attention to the manifold structure of data and shows good performance in clustering task. However, multi-view data usually contains noise, which reduces the robustness of the multi-view clusterin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c5a0ebf690eb0793c3528f4479722bb6
https://doi.org/10.1007/s11063-021-10634-3
https://doi.org/10.1007/s11063-021-10634-3
Publikováno v:
Applied Intelligence.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cbf972cec27c10b00446adfd8c3cf9c7
https://doi.org/10.1007/s10489-022-04267-9
https://doi.org/10.1007/s10489-022-04267-9
Autor:
Xiang Chen, Hongyan Liu, Qingjiang Xiao, Kaiwei Guo, Tingxin Sun, Xiang Ling, Xuan Liu, Qun Huang, Dong Zhang, Haifeng Zhou, Fan Zhang, Chunming Wu
Publikováno v:
2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::911e4cfa7ca9f6b70392f7c4cb1c6d32
https://doi.org/10.1109/icdcs54860.2022.00043
https://doi.org/10.1109/icdcs54860.2022.00043
Publikováno v:
IEEE Access, Vol 9, Pp 118851-118860 (2021)
In this paper, we present a hyper-Laplacian regularized method WHLR-MSC with a new weighted tensor nuclear norm for multi-view subspace clustering. Specifically, we firstly stack the subspace representation matrices of the different views into a tens
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9efedfad2b310045c54d360803bac779
https://doi.org/10.1109/access.2021.3107673
https://doi.org/10.1109/access.2021.3107673
Publikováno v:
2021 40th Chinese Control Conference (CCC).
The multi-view algorithm based on graph learning pays attention to the manifold structure of data and shows the good performance in clustering task. However, multi-view data usually contains noise, which reduces the robustness of multi-view clusterin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1be6a2a51efdc5fc55daefc53d05116f
https://doi.org/10.23919/ccc52363.2021.9549373
https://doi.org/10.23919/ccc52363.2021.9549373