Deep Nonnegative Matrix Factorization (deep NMF) was recently established to cope with the extraction of hierarchical latent feature representation, and it has been demonstrated to achieve outstanding results in unsupervised representation learning. However, defining a suitable regularization for the deep ...
To be more specific, we introduce an adversarial variant of the Deep NMF to improve the prediction of DMF in the network reconstruction task. This approach utilizes Deep NMF to encode the network within a low-dimensional space, and incorporates a modified adversarial training scheme to discover a...
Deep nonnegative matrix factorization (Deep NMF) as an emerging technique for image clustering has attracted more and more attention. This is because it can effectively reduce high-dimensional data and reveal the latent hierarchical information of the complex data. However, two limitations may still ...
Deep NMF-based Approaches F. Ye, C. Chen, and Z. Zheng. Deep autoencoder-like nonnegative matrix factorization for community detection. In CIKM, pages 1393–1402, 2018. Y. Li, C. Sha, X. Huang, and Y. Zhang. Community detection in attributed graphs: an embedding approach. In AAAI, ...
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We name this network architecture deep recurrent NMF (DR-NMF). The proposed DR-NMF network has three distinct advantages. First, DR-NMF provides better interpretability than other deep architectures, since the weights correspond to NMF model parameters, even after training. This interpretability also...
When r=1, the deep matrix factorization algorithm degrades to the traditional semi-NMF algorithm. As introduced in [32], such a multi-layer decomposition architecture is promising to deal with different types of attributes and to mine deep features of the data. Motivated by this, in our work...
main 1Branch Tags Code Folders and files Name Last commit message Last commit date Latest commit vleplat Update README.md Feb 5, 2024 6064f7e·Feb 5, 2024Feb 5, 2024 History 58 Commits Code/MatLab Update deepKL NMF Feb 2, 2024
对应的方法命名为MVC-DMF-PA:作者首先使用Deep Semi-NMF得到基划分矩阵,同时也从不同的视图中获取了特定的视图表示信息。接着,通过最优排列来最大化一致 划分矩阵 与 均匀加权的基划分矩阵 的对齐。最后,将base partition learning和 late fusion 统一到一个框架中,希望学习一个聚类的共识划分矩阵。
LDGRNMF.zip: Reference from "G. Li, J. Luo, C. Liang, Q. Xiao, P. Ding, and Y. Zhang, “Prediction of lncRNA-disease associations based on network consistency projection,” IEEE Access, vol. 7, pp. 58849-58856, 2019." LDA-LNSUBRW.zip: Reference from "G. Xie, J. Jiang, and...