Auto-weighted multi-view clustering via deep matrix decomposition
Real data are often collected from multiple channels or comprised of different
representations (ie, views). Multi-view learning provides an elegant way to analyze the multi …
representations (ie, views). Multi-view learning provides an elegant way to analyze the multi …
Robust bi-stochastic graph regularized matrix factorization for data clustering
Data clustering, which is to partition the given data into different groups, has attracted much
attention. Recently various effective algorithms have been developed to tackle the task …
attention. Recently various effective algorithms have been developed to tackle the task …
One step multi-view spectral clustering via joint adaptive graph learning and matrix factorization
W Yang, Y Wang, C Tang, H Tong, A Wei, X Wu - Neurocomputing, 2023 - Elsevier
Multi-view clustering based on graph learning has attracted extensive attention due to its
simplicity and efficiency in recent years. However, there are still some issues in most of the …
simplicity and efficiency in recent years. However, there are still some issues in most of the …
Non-negative matrix factorization with locality constrained adaptive graph
Non-negative matrix factorization (NMF) has recently attracted much attention due to its
good interpretation in perception science and widely applications in various fields. In this …
good interpretation in perception science and widely applications in various fields. In this …
Regularized nonnegative matrix factorization with adaptive local structure learning
Due to the effectiveness of Nonnegative Matrix Factorization (NMF) and its graph
regularized extensions, these methods have been received much attention from various …
regularized extensions, these methods have been received much attention from various …
Robust multi-view data clustering with multi-view capped-norm k-means
Real-world data sets are often comprised of multiple representations or views which provide
different and complementary aspects of information. Multi-view clustering is an important …
different and complementary aspects of information. Multi-view clustering is an important …
Self-weighted multi-view clustering with soft capped norm
Real-world data sets are often comprised of multiple representations or modalities which
provide different and complementary aspects of information. Multi-view clustering plays an …
provide different and complementary aspects of information. Multi-view clustering plays an …
Robust graph regularized nonnegative matrix factorization for clustering
Nonnegative matrix factorization and its graph regularized extensions have received
significant attention in machine learning and data mining. However, existing approaches are …
significant attention in machine learning and data mining. However, existing approaches are …
Robust nonnegative matrix factorization with structure regularization
Q Huang, X Yin, S Chen, Y Wang, B Chen - Neurocomputing, 2020 - Elsevier
Nonnegative matrix factorization (NMF) has attracted more and more attention due to its
wide applications in computer vision, information retrieval, and machine learning. In contrast …
wide applications in computer vision, information retrieval, and machine learning. In contrast …
Adaptive local structure learning for document co-clustering
The goal of document co-clustering is to partition textual data sets into groups by utilizing the
duality between documents (ie, data points) and words (ie, features). That is, the documents …
duality between documents (ie, data points) and words (ie, features). That is, the documents …