Robust semi-supervised data representation and imputation by correntropy based constraint nonnegative matrix factorization

Zhou, Nan; Du, Yuanhua; Li, Jun; Huang, Xiuyu; Shen, Xiao; Choi, Kup‐Sze

doi:10.1007/s10489-022-03884-8

Cited by 3 publications

(1 citation statement)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In practical applications, matrix factorization has to account for noise in the environment. To improve the stability of matrix factorization, algorithms minimize the accumulation and amplification of noise in the computation to provide reliable results [17][18][19][20][21][22]. With the development of neural dynamics in various fields [23][24][25][26][27][28][29][30], researchers turn their attention to matrix factorization.…”

Section: Introductionmentioning

confidence: 99%

AREND: A Faster and Robust Solution for Dynamic LR and QR Factorization

Zhuang,

He,

et al. 2024

Preprint

View full text Add to dashboard Cite

Dynamic LR and QR factorization are fundamental problems that exist widely in the control field. However, the existing solutions under noises are lack of convergence speed and anti-interference ability. To this end, this paper incorporates the advantages of Dynamic-Coefficient Type (DCT) and Integration-Enhance Type (IET) Zeroing Neural Dynamic (ZND), and proposes an Adaptive and Robust-Enhanced Neural Dynamic (AREND). On this basis, a Strategy of Integration-Coupling (SIC) is proposed to address multiple error function problems, improving model stability and application scenarios. This strategy is experimentally proven to be effective and has potential expansion capability. After that, the convergence and robustness of our AREND is theoretically analyzed. Furthermore, the proposed AREND is verified by numerical experiments of low-to-high dimensional factorization in comparison with existing solutions. Finally, the real-time 3-D Angle of Arrival (AoA) localization in multiple high-noise conditions, is validated to the accuracy of the proposed model. Code is available at https://github.com/Alana2a3/AREND-Code-Implementation.

show abstract

Section: Introductionmentioning

confidence: 99%

AREND: A Faster and Robust Solution for Dynamic LR and QR Factorization

Zhuang,

He,

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

Robust sparse graph regularized nonnegative matrix factorization for automatic depression diagnosis

Zhang,

Zhong,

Wang

et al. 2024

Biomedical Signal Processing and Control

View full text Add to dashboard Cite

Towards faster and robust solution for dynamic LR and QR factorization

Zhuang,

He,

et al. 2024

Sci Rep

View full text Add to dashboard Cite

Dynamic LR and QR factorization are fundamental problems that exist widely in the control field. However, the existing solutions under noises are lack of convergence speed and anti-noise ability. To this end, this paper incorporates the advantages of Dynamic-Coefficient Type (DCT) and Integration-Enhance Type (IET) Zeroing Neural Dynamic (ZND), and proposes an Adaptive and Robust-Enhanced Neural Dynamic (AREND). On this basis, a Strategy of Integration-Coupling (SIC) is proposed to address multiple error function problems, improving model stability and application scenarios. This strategy is experimentally proven to be effective and has potential expansion capability. After that, the convergence and robustness of our AREND is theoretically analyzed. Furthermore, the proposed AREND is verified by numerical experiments of low-to-high dimensional factorization in comparison with existing solutions. Finally, the real-time 3-D Angle of Arrival (AoA) localization in multiple high-noise conditions, is validated to the accuracy of the proposed model. Code is available at https://github.com/Alana2a3/AREND-Code-Implementation .

show abstract

Robust semi-supervised data representation and imputation by correntropy based constraint nonnegative matrix factorization

Cited by 3 publications

References 48 publications

AREND: A Faster and Robust Solution for Dynamic LR and QR Factorization

AREND: A Faster and Robust Solution for Dynamic LR and QR Factorization

Robust sparse graph regularized nonnegative matrix factorization for automatic depression diagnosis

Towards faster and robust solution for dynamic LR and QR factorization

Contact Info

Product

Resources

About