RT Journal Article
T1 Exploratory Data Analysis and Foreground Detection with the Growing Hierarchical Neural Forest.
A1 Palomo-Ferrer, Esteban José
A1 López-Rubio, Ezequiel
A1 Ortega-Zamorano, Francisco
A1 Benítez-Rochel, Rafaela
K1 Sistemas autoorganizativos
K1 Procesado de imágenes
K1 Análisis cluster
K1 Minería de datos
K1 Inteligencia artificial
K1 Redes neuronales (Informática)
AB In this paper, a new self-organizing artificial neural network called growing hierarchical neural forest (GHNF) is proposed. The GHNF is a hierarchical model based on the growing neural forest, which is a tree-based model that learns a set of trees (forest) instead of a general graph so that the forest can grow in size. This way, the GHNF faces three important limitations regarding the self-organizing map: fixed size, fixed topology, and lack of hierarchical representation for input data. Hence, the GHNF is especially amenable to datasets containing clusters where each cluster has a hierarchical structure since each tree of the GHNF forest can adapt to one of the clusters. Experimental results show the goodness of our proposal in terms of self-organization and clustering capabilities. In particular, it has been applied to text mining of tweets as a typical exploratory data analysis application, where a hierarchical representation of concepts present in tweets has been obtained. Moreover, it has been applied to foreground detection in video sequences, outperforming several methods specialized in foreground detection.
PB Springer
YR 2020
FD 2020
LK https://hdl.handle.net/10630/39416
UL https://hdl.handle.net/10630/39416
LA eng
NO Palomo, E.J., López-Rubio, E., Ortega-Zamorano, F. et al. Exploratory Data Analysis and Foreground Detection with the Growing Hierarchical Neural Forest. Neural Process Lett 52, 2537–2563 (2020). https://doi.org/10.1007/s11063-020-10360-2
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 21 ene 2026