Robust spectral embedded bilateral orthogonal concept factorization for clustering

Robust spectral embedded bilateral orthogonal concept factorization for clustering

Concept factorization (CF), unlike nonnegative matrix factorization (NMF), can handle data with negative values by approximating the original data with two low-dimensional nonnegative matrices and itself. Nevertheless, existing CF-based methods continue to suffer from the two issues specified as fol...

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Bibliographic Details
Main Authors: Yang, Ben, Wu, Jinghan, Zhou, Yu, Zhang, Xuetao, Lin, Zhiping, Nie, Feiping, Chen, Badong
Other Authors: School of Electrical and Electronic Engineering
Format: Journal Article
Language:English
Published: 2024
Subjects:
Engineering
Concept factorization
Spectral embedding
Online Access:https://hdl.handle.net/10356/180168

Internet

https://hdl.handle.net/10356/180168

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