Learning Topologies with the Growing Neural Forest.
In this work, a novel self-organizing model called growing neural forest (GNF) is presented. It is based on the growing neural gas (GNG), which learns a general graph with no special provisions for datasets with separated clusters. On the contrary, the proposed GNF learns a set of trees so that each tree represents a connected cluster of data. High dimensional datasets often contain large empty regions among clusters, so this proposal is better suited to them than other self-organizing models because it represents these separated clusters as connected components made of neurons. Experimental results are reported which show the self-organization capabilities of the model. Moreover, its suitability for unsupervised clustering and foreground detection applications is demonstrated. In particular, the GNF is shown to correctly discover the connected component structure of some datasets. Moreover, it outperforms some well-known foreground detectors both in quantitative and qualitative terms.
* Department of Computer Languages and Computer Science, University of Málaga, Bulevar Louis Pasteur 35, 29071 Málaga, Spain.|† School of Mathematics and Computer Science, University of Yachay Tech, Ecuador.
Esteban José
EJ
Palomo
* Department of Computer Languages and Computer Science, University of Málaga, Bulevar Louis Pasteur 35, 29071 Málaga, Spain.
Ezequiel
E
López-Rubio
D016000:Cluster Analysis; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D016571:Neural Networks, Computer; D000069558:Unsupervised Machine Learning
Self-organization; computer vision; tree-structured model; unsupervised clustering