Cargando…
Kernel Analysis Based on Dirichlet Processes Mixture Models
Kernels play a crucial role in Gaussian process regression. Analyzing kernels from their spectral domain has attracted extensive attention in recent years. Gaussian mixture models (GMM) are used to model the spectrum of kernels. However, the number of components in a GMM is fixed. Thus, this model s...
Autores principales: | Tian, Jinkai, Yan, Peifeng, Huang, Da |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7515386/ http://dx.doi.org/10.3390/e21090857 |
Ejemplares similares
-
Dirichlet process mixture models for single-cell RNA-seq clustering
por: Adossa, Nigatu A., et al.
Publicado: (2022) -
Dynamic classification of fetal heart rates by hierarchical Dirichlet process mixture models
por: Yu, Kezi, et al.
Publicado: (2017) -
Dirichlet Multinomial Mixtures: Generative Models for Microbial Metagenomics
por: Holmes, Ian, et al.
Publicado: (2012) -
Clustering compositional data using Dirichlet mixture model
por: Pal, Samyajoy, et al.
Publicado: (2022) -
From here to infinity: sparse finite versus Dirichlet process mixtures in model-based clustering
por: Frühwirth-Schnatter, Sylvia, et al.
Publicado: (2018)