Hypergraph based semi-supervised symmetric nonnegative matrix factorization for image clustering
Semi-supervised symmetric nonnegative matrix factorization (SNMF) has been shown to be a significant method for both linear and nonlinear data clustering applications. Nevertheless, existing SNMF-based methods only adopt a simple graph to construct the similarity matrix, and cannot fully use the lim...
Saved in:
Main Authors: | Yin, Jingxing, Peng, Siyuan, Yang, Zhijing, Chen, Badong, Lin, Zhiping |
---|---|
Other Authors: | School of Electrical and Electronic Engineering |
Format: | Article |
Language: | English |
Published: |
2023
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/172037 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
Dual semi-supervised convex nonnegative matrix factorization for data representation
by: Peng, Siyuan, et al.
Published: (2022) -
Robust semi-supervised nonnegative matrix factorization for image clustering
by: Peng, Siyuan, et al.
Published: (2022) -
Semi-supervised spam detection in Twitter stream
by: Sedhai, Surendra, et al.
Published: (2018) -
Cognitive workload quantification for air traffic controllers: an ensemble semi-supervised learning approach
by: Yu, Xiaoqing, et al.
Published: (2025) -
Robust semi-supervised federated learning for images automatic recognition in internet of drones
by: Zhang, Zhe, et al.
Published: (2023)