Search

Your search keyword '"Leng, Chengcai"' showing total 135 results
Start Over Author "Leng, Chengcai"
135 results on '"Leng, Chengcai"'

11. A Novel Data Clustering Method Based on Smooth Non-negative Matrix Factorization

Author

Leng, Chengcai, Zhang, Hai, Cai, Guorong, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Basu, Anup, editor, and Berretti, Stefano, editor

Published
2018
Full Text
View/download PDF

12. Predicting Student Seating Distribution Based on Social Affinity

Author

Pei, Zhao, Pan, Miaomiao, Liao, Kang, Ma, Miao, Leng, Chengcai, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Basu, Anup, editor, and Berretti, Stefano, editor

Published
2018
Full Text
View/download PDF

14. Dual-Graph Global and Local Concept Factorization for Data Clustering

Author

Li, Ning, Leng, Chengcai, Cheng, Irene, Basu, Anup, and Jiao, Licheng

Abstract

Considering a wide range of applications of nonnegative matrix factorization (NMF), many NMF and their variants have been developed. Since previous NMF methods cannot fully describe complex inner global and local manifold structures of the data space and extract complex structural information, we propose a novel NMF method called dual-graph global and local concept factorization (DGLCF). To properly describe the inner manifold structure, DGLCF introduces the global and local structures of the data manifold and the geometric structure of the feature manifold into CF. The global manifold structure makes the model more discriminative, while the two local regularization terms simultaneously preserve the inherent geometry of data and features. Finally, we analyze convergence and the iterative update rules of DGLCF. We illustrate clustering performance by comparing it with latest algorithms on four real-world datasets.

Published
2024
Full Text
View/download PDF

16. A Smooth Approximation Algorithm of Rank-Regularized Optimization Problem and Its Applications

Author

Li, Bo, Jin, Lianbao, Leng, Chengcai, Lu, Chunyuan, Zhang, Jie, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, and Zhang, Yu-Jin, editor

Published
2015
Full Text
View/download PDF

18. Hybrid Conv-ViT Network for Hyperspectral Image Classification.

Author

Yan, Huaiping, Zhang, Erlei, Wang, Jun, Leng, Chengcai, Basu, Anup, and Peng, Jinye

Abstract

With the success of Vision Transformer (ViT), Transformer is being increasingly used for hyperspectral image (HSI) classification given its ability to extract global context dependencies. However, existing methods based on transformers tend to classify HSI in the traditional patch-wise manner. Thus, these methods cannot obtain true global features because the inputs of the model are local patches. To solve these problems, a hybrid convolution and ViT network (HCVN) is proposed for HSI classification. HCVN realizes the classification task from the perspective of semantic segmentation, and its input is the entire HSI, which makes it possible to obtain truly meaningful global features. By improving the original ViT, an HCV module is proposed, which enhances the ability of local structure characterization while extracting global features. The HCVN hybrid convolution layer and HCV module realize the extraction and fusion of local and global features. Finally, the dual branch network architecture is used to integrate the spatial and spectral features. Extensive experiments on two datasets verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Published
2023
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results