.png){kind=icon}
.png){kind=icon}
Return
Parameter-less Auto-weighted multiple graph regularized Nonnegative Matrix Factorization for data representation
.png){kind=icon}
PRE
AI
DOI：10.1016/j.knosys.2017.05.029.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Recently, multiple graph regularizer based methods have shown promising performances in data representation. However, the parameter choice of the regularizer is crucial to the performance of clustering and its optimal value changes for different real datasets. To deal with this problem, we propose a novel method called Parameter-less Auto-weighted Multiple Graph regularized Nonnegative Matrix Factorization (PAMGNMF) in this paper. PAMGNMF employs the linear combination of multiple simple graphs to approximate the manifold structure of data as previous methods do. Moreover, the proposed method can automatically learn an optimal weight for each graph without introducing an additive parameter. Therefore, the proposed PAMGNMF method is easily applied to practical problems. Extensive experimental results on different real-world datasets have demonstrated that the proposed method achieves better performance than the state-of-the-art approaches. (C) 2017 Elsevier B.V. All rights reserved.
Keywords:
Data representation
Multiple graph
NMF
Parameter-less
Manifold
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
K
IF:
7.6
Papers:
1.2W
Citations:
4.5W