Paper Info
Title
Exploratory Data Analysis And Foreground Detection With The Growing Hierarchical Neural Forest
Abstract
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.
Year
DOI
Venue
2020
10.1007/s11063-020-10360-2
NEURAL PROCESSING LETTERS
Keywords
DocType
Volume
Self-organization, Clustering, Text mining, Image segmentation
Journal
52
Issue
ISSN
Citations 
3
1370-4621
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Esteban J. Palomo19514.79
Ezequiel López-Rubio232339.73
Francisco Ortega-Zamorano3648.19
Rafaela Benítez-Rochel400.34