Cargando…
The Optimally Designed Variational Autoencoder Networks for Clustering and Recovery of Incomplete Multimedia Data
Clustering analysis of massive data in wireless multimedia sensor networks (WMSN) has become a hot topic. However, most data clustering algorithms have difficulty in obtaining latent nonlinear correlations of data features, resulting in a low clustering accuracy. In addition, it is difficult to extr...
Autores principales: | Yu, Xiulan, Li, Hongyu, Zhang, Zufan, Gan, Chenquan |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6413117/ https://www.ncbi.nlm.nih.gov/pubmed/30781499 http://dx.doi.org/10.3390/s19040809 |
Ejemplares similares
-
Constrained Bayesian optimization for automatic chemical design using variational autoencoders
por: Griffiths, Ryan-Rhys, et al.
Publicado: (2019) -
Variational Autoencoders for Cancer Data Integration: Design Principles and Computational Practice
por: Simidjievski, Nikola, et al.
Publicado: (2019) -
Optimizing Variational Graph Autoencoder for Community Detection with Dual Optimization
por: Choong, Jun Jin, et al.
Publicado: (2020) -
bmVAE: a variational autoencoder method for clustering single-cell mutation data
por: Yan, Jiaqian, et al.
Publicado: (2022) -
Optimizing Few-Shot Learning Based on Variational Autoencoders
por: Wei, Ruoqi, et al.
Publicado: (2021)