A survey on deep matrix factorizations
Constrained low-rank matrix approximations have been known for decades as powerful
linear dimensionality reduction techniques able to extract the information contained in large …
linear dimensionality reduction techniques able to extract the information contained in large …
ASRNN: A recurrent neural network with an attention model for sequence labeling
Natural language processing (NLP) is useful for handling text and speech, and sequence
labeling plays an important role by automatically analyzing a sequence (text) to assign …
labeling plays an important role by automatically analyzing a sequence (text) to assign …
The rise of nonnegative matrix factorization: algorithms and applications
YT Guo, QQ Li, CS Liang - Information Systems, 2024 - Elsevier
Although nonnegative matrix factorization (NMF) is widely used, some matrix factorization
methods result in misleading results and waste of computing resources due to lack of timely …
methods result in misleading results and waste of computing resources due to lack of timely …
Symmetric nonnegative matrix factorization-based community detection models and their convergence analysis
Community detection is a popular yet thorny issue in social network analysis. A symmetric
and nonnegative matrix factorization (SNMF) model based on a nonnegative multiplicative …
and nonnegative matrix factorization (SNMF) model based on a nonnegative multiplicative …
A survey of deep nonnegative matrix factorization
WS Chen, Q Zeng, B Pan - Neurocomputing, 2022 - Elsevier
Abstract Deep Nonnegative Matrix Factorization (Deep NMF) is an effective strategy for
feature extraction in recent years. By decomposing the matrix recurrently on account of the …
feature extraction in recent years. By decomposing the matrix recurrently on account of the …
Discriminative subspace matrix factorization for multiview data clustering
In a real-world scenario, an object is easily considered as features combined by multiple
views in reality. Thus, multiview features can be encoded into a unified and discriminative …
views in reality. Thus, multiview features can be encoded into a unified and discriminative …
Self-supervised semi-supervised nonnegative matrix factorization for data clustering
Semi-supervised nonnegative matrix factorization exploits the strengths of matrix
factorization in successfully learning part-based representation and is also able to achieve …
factorization in successfully learning part-based representation and is also able to achieve …
Multi-view clustering via deep concept factorization
Recent studies have shown the satisfactory results of the matrix factorization technique in
Multi-view Clustering (MVC). Compared with the single-layer formed clustering models, the …
Multi-view Clustering (MVC). Compared with the single-layer formed clustering models, the …
Diverse deep matrix factorization with hypergraph regularization for multi-view data representation
Deep matrix factorization (DMF) has been demon-strated to be a powerful tool to take in the
complex hierarchical information of multi-view data (MDR). However, existing multi-view …
complex hierarchical information of multi-view data (MDR). However, existing multi-view …
Global and local similarity learning in multi-kernel space for nonnegative matrix factorization
Most of existing nonnegative matrix factorization (NMF) methods do not fully exploit global
and local similarity information from data. In this paper, we propose a novel local similarity …
and local similarity information from data. In this paper, we propose a novel local similarity …