Multi-view learning overview: Recent progress and new challenges
Multi-view learning is an emerging direction in machine learning which considers learning
with multiple views to improve the generalization performance. Multi-view learning is also …
with multiple views to improve the generalization performance. Multi-view learning is also …
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 …
Multi-view deep network: a deep model based on learning features from heterogeneous neural networks for sentiment analysis
By the development of social media, sentiment analysis has changed to one of the most
remarkable research topics in the field of natural language processing which tries to dig …
remarkable research topics in the field of natural language processing which tries to dig …
Multi-view hypergraph regularized Lp norm least squares twin support vector machines for semi-supervised learning
J Lu, X **e, Y **ong - Pattern Recognition, 2024 - Elsevier
In recent years, multi-view semi-supervised learning has gradually become a popular
research direction. The classic binary classification methods in this field are multi-view …
research direction. The classic binary classification methods in this field are multi-view …
A novel approach for classification of mental tasks using multiview ensemble learning (MEL)
Brain-computer interface (BCI) is a domain, in which a person can send information without
using any exterior nerve or muscles, just using their brain signal, called …
using any exterior nerve or muscles, just using their brain signal, called …
Multi-view consensus proximity learning for clustering
Most proximity-based multi-view clustering methods are sensitive to the initial proximity
matrix, where the clustering performance is quite unstable when using different initial …
matrix, where the clustering performance is quite unstable when using different initial …
Feature concatenation for adversarial domain adaptation
J Li, Z Li, S Lü - Expert Systems with Applications, 2021 - Elsevier
Abstract Domain adaptation aims to mitigate the domain gap between the source and target
domains so that knowledge can be transferred between domains. There are two key factors …
domains so that knowledge can be transferred between domains. There are two key factors …
Multilinear algebra methods for higher-dimensional graphs
In this paper, we will explore the use of multilinear algebra-based methods for higher
dimensional graphs. Multi-view clustering (MVC) has gained popularity over the single-view …
dimensional graphs. Multi-view clustering (MVC) has gained popularity over the single-view …
HarMI: Human activity recognition via multi-modality incremental learning
Nowadays, with the development of various kinds of sensors in smartphones or wearable
devices, human activity recognition (HAR) has been widely researched and has numerous …
devices, human activity recognition (HAR) has been widely researched and has numerous …
Multiview Large Margin Distribution Machine
K Hu, Y **ao, W Zheng, W Zhu… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Margin distribution has been proven to play a crucial role in improving generalization ability.
In recent studies, many methods are designed using large margin distribution machine …
In recent studies, many methods are designed using large margin distribution machine …