Uniform distribution non-negative matrix factorization for multiview clustering
Multiview data processing has attracted sustained attention as it can provide more
information for clustering. To integrate this information, one often utilizes the non-negative …
information for clustering. To integrate this information, one often utilizes the non-negative …
Multi-view data clustering via non-negative matrix factorization with manifold regularization
Nowadays, non-negative matrix factorization (NMF) based cluster analysis for multi-view
data shows impressive behavior in machine learning. Usually, multi-view data have …
data shows impressive behavior in machine learning. Usually, multi-view data have …
Robust multi-view non-negative matrix factorization for clustering
X Liu, P Song, C Sheng, W Zhang - Digital Signal Processing, 2022 - Elsevier
Non-negative matrix factorization (NMF) has attracted much attention for multi-view
clustering due to its good theoretical and practical values. Although existing multi-view NMF …
clustering due to its good theoretical and practical values. Although existing multi-view NMF …
Dual-graph regularized concept factorization for multi-view clustering
Matrix factorization is an important technology that obtains the latent representation of data
by mining the potential structure of data. As two popular matrix factorization techniques …
by mining the potential structure of data. As two popular matrix factorization techniques …
Co-learning non-negative correlated and uncorrelated features for multi-view data
L Zhao, T Yang, J Zhang, Z Chen… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Multi-view data can represent objects from different perspectives and thus provide
complementary information for data analysis. A topic of great importance in multi-view …
complementary information for data analysis. A topic of great importance in multi-view …
Nonredundancy regularization based nonnegative matrix factorization with manifold learning for multiview data representation
G Cui, Y Li - Information Fusion, 2022 - Elsevier
In the real world, one object is usually described via multiple views or modalities. Many
existing multiview clustering methods fuse the information of multiple views by learning a …
existing multiview clustering methods fuse the information of multiple views by learning a …
A multiple association-based unsupervised feature selection algorithm for mixed data sets
Companies have an increasing access to very large datasets within their domain. Analysing
these datasets often requires the application of feature selection techniques in order to …
these datasets often requires the application of feature selection techniques in order to …
Semi-supervised multi-view clustering based on constrained nonnegative matrix factorization
H Cai, B Liu, Y Xiao, LY Lin - Knowledge-Based Systems, 2019 - Elsevier
Most existing clustering approaches address multi-view clustering problems by graph
regularized nonnegative matrix factorization to obtain the new representation of each view …
regularized nonnegative matrix factorization to obtain the new representation of each view …
Semi-supervised multi-view concept decomposition
Abstract Concept Factorization (CF), as a novel paradigm of representation learning, has
demonstrated superior performance in multi-view clustering tasks. It overcomes limitations …
demonstrated superior performance in multi-view clustering tasks. It overcomes limitations …
Unsupervised multi-view non-negative for law data feature learning with dual graph-regularization in smart Internet of Things
X Qiu, Z Chen, L Zhao, C Hu - Future Generation Computer Systems, 2019 - Elsevier
In the real world, the law data in the smart Internet of Things usually consists of
heterogeneous information with some noises. Non-negative matrix factorization is a popular …
heterogeneous information with some noises. Non-negative matrix factorization is a popular …