Sample weighted multiple kernel k-means via min-max optimization
A representative multiple kernel clustering (MKC) algorithm, termed simple multiple kernel k-
means (SMKKM), is recently proposed to optimally mine useful information from a set of pre …
means (SMKKM), is recently proposed to optimally mine useful information from a set of pre …
Consistent affinity representation learning with dual low-rank constraints for multi-view subspace clustering
Multi-view clustering aims to achieve better accuracy of data clustering by leveraging
complementary information embedded in multi-view data. How to learn a consistent …
complementary information embedded in multi-view data. How to learn a consistent …
Self-weighted graph learning for multi-view clustering
X Shu, X Zhang, Q Wang - Neurocomputing, 2022 - Elsevier
The graph-based multi-view clustering has received extensive attention in recent years due
to its competitiveness in characterizing the relationship between data and its well defined …
to its competitiveness in characterizing the relationship between data and its well defined …
Clustering of noised and heterogeneous multi-view data with graph learning and projection decomposition
H Wang, W Zhang, X Ma - Knowledge-Based Systems, 2022 - Elsevier
Complex systems in society and nature cannot be effectively modeled and represented by a
single perspective, resulting in the so-called multi-view data, which provide an absolutely …
single perspective, resulting in the so-called multi-view data, which provide an absolutely …
Low-rank constraint bipartite graph learning
Bipartite Graph-based multi-view clustering has become an active topic recently due to its
efficiency in tackling large scale multi-view data. However, most existing bipartite graph …
efficiency in tackling large scale multi-view data. However, most existing bipartite graph …
Preserving bilateral view structural information for subspace clustering
Subspace clustering algorithms have been found successful in various applications that
involve two-dimensional data, ie, each example of the data is a matrix. However, most of the …
involve two-dimensional data, ie, each example of the data is a matrix. However, most of the …
Multi-view clustering for integration of gene expression and methylation data with tensor decomposition and self-representation learning
X Gao, Y Wang, W Hou, Z Liu… - IEEE/ACM Transactions on …, 2022 - ieeexplore.ieee.org
The accumulated DNA methylation and gene expression provide a great opportunity to
exploit the epigenetic patterns of genes, which is the foundation for revealing the underlying …
exploit the epigenetic patterns of genes, which is the foundation for revealing the underlying …
Fractional graph coloring for functional compression with side information
D Malak - 2022 IEEE Information Theory Workshop (ITW), 2022 - ieeexplore.ieee.org
We describe a rational approach to reduce the computational and communication
complexities of lossless point-to-point compression for computation with side information …
complexities of lossless point-to-point compression for computation with side information …
Multi-view clustering with graph regularized optimal transport
For a multi-view learning task, it is crucial to assign appropriate weights to each view in
order to learn complementary and consistent information across different views. In the field …
order to learn complementary and consistent information across different views. In the field …
Bilateral fast low-rank representation with equivalent transformation for subspace clustering
Q Shen, S Yi, Y Liang, Y Chen… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
In recent years, low-rank representation (LRR) has received increasing attention on
subspace clustering. Due to inevitable matrix inversion and singular value decomposition in …
subspace clustering. Due to inevitable matrix inversion and singular value decomposition in …