Simultaneous global and local graph structure preserving for multiple kernel clustering
Z Ren, Q Sun - IEEE transactions on neural networks and …, 2020 - ieeexplore.ieee.org
Multiple kernel learning (MKL) is generally recognized to perform better than single kernel
learning (SKL) in handling nonlinear clustering problem, largely thanks to MKL avoids …
learning (SKL) in handling nonlinear clustering problem, largely thanks to MKL avoids …
Phase stability through machine learning
R Arróyave - Journal of Phase Equilibria and Diffusion, 2022 - Springer
Understanding the phase stability of a chemical system constitutes the foundation of
materials science. Knowledge of the equilibrium state of a system under arbitrary …
materials science. Knowledge of the equilibrium state of a system under arbitrary …
A new approach for mining correlated frequent subgraphs
Nowadays graphical datasets are having a vast amount of applications. As a result, graph
mining—mining graph datasets to extract frequent subgraphs—has proven to be crucial in …
mining—mining graph datasets to extract frequent subgraphs—has proven to be crucial in …
Graph multiview canonical correlation analysis
Multiview canonical correlation analysis (MCCA) seeks latent low-dimensional
representations encountered with multiview data of shared entities (aka common sources) …
representations encountered with multiview data of shared entities (aka common sources) …
Graph-guided unsupervised multiview representation learning
Without the valuable label information to guide the learning process, it is demanding to fully
excavate and integrate the underlying information from different views to learn the unified …
excavate and integrate the underlying information from different views to learn the unified …
MCoCo: Multi-level Consistency Collaborative multi-view clustering
Multi-view clustering can explore consistent information from different views to guide
clustering. Most existing works focus on pursuing shallow consistency in the feature space …
clustering. Most existing works focus on pursuing shallow consistency in the feature space …
Learning canonical f-correlation projection for compact multiview representation
Canonical correlation analysis (CCA) matters in multiview representation learning. But, CCA
and its most variants are essentially based on explicit or implicit covariance matrices. It …
and its most variants are essentially based on explicit or implicit covariance matrices. It …
Robust generalized canonical correlation analysis
Generalized canonical correlation analysis (GCCA) has been widely used for classification
and regression problems. The key idea of GCCA is to map the data from different views into …
and regression problems. The key idea of GCCA is to map the data from different views into …
A joint constrained CCA model for network-dependent brain subregion parcellation
Connectivity-based brain region parcellation from functional magnetic resonance imaging
(fMRI) data is complicated by heterogeneity among aged and diseased subjects, particularly …
(fMRI) data is complicated by heterogeneity among aged and diseased subjects, particularly …
BayReL: Bayesian relational learning for multi-omics data integration
E Hajiramezanali, A Hasanzadeh… - Advances in …, 2020 - proceedings.neurips.cc
High-throughput molecular profiling technologies have produced high-dimensional multi-
omics data, enabling systematic understanding of living systems at the genome scale …
omics data, enabling systematic understanding of living systems at the genome scale …