Breaking down multi-view clustering: a comprehensive review of multi-view approaches for complex data structures
Abstract Multi-View Clustering (MVC) is an emerging research area aiming to cluster
multiple views of the same data, which has recently drawn substantial attention. Various …
multiple views of the same data, which has recently drawn substantial attention. Various …
A multi-view clustering algorithm based on deep semi-NMF
Multi-view clustering (MVC) aims to fuse the information among multiple views to achieve
effective clustering. Many MVC algorithms based on semi-nonnegative matrix factorization …
effective clustering. Many MVC algorithms based on semi-nonnegative matrix factorization …
An autoencoder-like deep NMF representation learning algorithm for clustering
Clustering plays a crucial role in the field of data mining, where deep non-negative matrix
factorization (NMF) has attracted significant attention due to its effective data representation …
factorization (NMF) has attracted significant attention due to its effective data representation …
Artificial intelligence algorithms in flood prediction: a general overview
M Pandey - Geo-information for Disaster Monitoring and …, 2024 - Springer
This paper presents a comprehensive general overview of the extensive literature available
in the field of application of artificial intelligence (AI) in flood prediction. The initial approach …
in the field of application of artificial intelligence (AI) in flood prediction. The initial approach …
Exclusivity and consistency induced NMF for multi-view representation learning
Many unsupervised multi-view representation learning (MRL) techniques have been
devised as multi-view data becomes more common in real-world applications. However …
devised as multi-view data becomes more common in real-world applications. However …
Clean affinity matrix induced hyper-Laplacian regularization for unsupervised multi-view feature selection
Most previous unsupervised multi-view feature selection (UMFS) methods have achieved
appealing performance by exploring the consistency among multiple views. However, they …
appealing performance by exploring the consistency among multiple views. However, they …
Bayesian non-negative matrix factorization with Student's t-distribution for outlier removal and data clustering
R Yuan, C Leng, S Zhang, J Peng, A Basu - Engineering Applications of …, 2024 - Elsevier
Abstract Non-negative Matrix Factorization (NMF) is an effective way to solve the
redundancy of non-negative high-dimensional data. Most of the traditional probability-based …
redundancy of non-negative high-dimensional data. Most of the traditional probability-based …
A Lightweight Anchor-Based Incremental Framework for Multi-view Clustering
The rapid development of multi-media techniques boosts the emergence of multi-view data,
and how to uncover its intrinsic structure and utilize it to conduct the subsequent …
and how to uncover its intrinsic structure and utilize it to conduct the subsequent …
Critical factors influencing live birth rates in fresh embryo transfer for IVF: insights from cluster ensemble algorithms
Z Yu, X Zheng, J Sun, P Zhang, Y Zhong, X Lv… - Scientific Reports, 2025 - nature.com
Infertility has emerged as a significant global health concern. Assisted reproductive
technology (ART) assists numerous infertile couples in conceiving, yet some experience …
technology (ART) assists numerous infertile couples in conceiving, yet some experience …
Small stochastic data compactification concept justified in the entropy basis
Measurement is a typical way of gathering information about an investigated object,
generalized by a finite set of characteristic parameters. The result of each iteration of the …
generalized by a finite set of characteristic parameters. The result of each iteration of the …