Dual semi-supervised convex nonnegative matrix factorization for data representation

Dual semi-supervised convex nonnegative matrix factorization for data representation

Semi-supervised nonnegative matrix factorization (NMF) has received considerable attention in machine learning and data mining. A new semi-supervised NMF method, called dual semi-supervised convex nonnegative matrix factorization (DCNMF), is proposed in this paper for fully using the limited label i...

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Bibliographic Details
Main Authors: Peng, Siyuan, Yang, Zhijing, Ling, Bingo Wing-Kuen, Chen, Badong, Lin, Zhiping
Other Authors: School of Electrical and Electronic Engineering
Format: Journal Article
Language:English
Published: 2022
Subjects:
Engineering::Electrical and electronic engineering
Semi-Supervised Learning
Data Representation
Online Access:https://hdl.handle.net/10356/161773

Internet

https://hdl.handle.net/10356/161773

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