Generalized latent multi-view clustering with tensorized bipartite graph.
Neural Netw
; 175: 106282, 2024 Jul.
Article
de En
| MEDLINE
| ID: mdl-38599137
ABSTRACT
Tensor-based multi-view spectral clustering algorithms use tensors to model the structure of multi-dimensional data to take advantage of the complementary information and high-order correlations embedded in the graph, thus achieving impressive clustering performance. However, these algorithms use linear models to obtain consensus, which prevents the learned consensus from adequately representing the nonlinear structure of complex data. In order to address this issue, we propose a method called Generalized Latent Multi-View Clustering with Tensorized Bipartite Graph (GLMC-TBG). Specifically, in this paper we introduce neural networks to learn highly nonlinear mappings that encode nonlinear structures in graphs into latent representations. In addition, multiple views share the same latent consensus through nonlinear interactions. In this way, a more comprehensive common representation from multiple views can be achieved. An Augmented Lagrangian Multiplier with Alternating Direction Minimization (ALM-ADM) framework is designed to optimize the model. Experiments on seven real-world data sets verify that the proposed algorithm is superior to state-of-the-art algorithms.
Mots clés
Texte intégral:
1
Collection:
01-internacional
Base de données:
MEDLINE
Sujet principal:
Algorithmes
/
29935
Limites:
Humans
Langue:
En
Journal:
Neural Netw
/
Neural netw
/
Neural networks
Sujet du journal:
NEUROLOGIA
Année:
2024
Type de document:
Article
Pays de publication:
États-Unis d'Amérique