Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/89955
Type: Artigo de evento
Title: Using Unsupervised Learning For Graph Construction In Semi-supervised Learning With Graphs
Author: Escalante D.A.C.
Taubin G.
Nonato L.G.
Goldenstein S.K.
Abstract: Semi-supervised Learning with Graphs can achieve good results in classification tasks even in difficult conditions. Unfortunately, it can be slow and use a lot of memory. The first important step of the graph-based semi-supervised learning approaches is the construction of the graph from the data, where each data-point usually becomes a vertex in the graph - a potential problem with large amounts of data. In this paper, we present a graph construction method that uses an unsupervised neural network called growing neural gas (GNG). The GNG instance presents a intelligent stopping criteria that determines when the final network configuration maps correctly the input-data points. With that in mind, we use the final trained network as a reduced input graph for the semi-supervised classification algorithm, associating original data-points to the neurons they have activated in the unsupervised training process. © 2013 IEEE.
Editor: 
Rights: fechado
Identifier DOI: 10.1109/SIBGRAPI.2013.13
Address: http://www.scopus.com/inward/record.url?eid=2-s2.0-84891515364&partnerID=40&md5=85cb79f6e0d6e709603ce37736b0d298
Date Issue: 2013
Appears in Collections:Unicamp - Artigos e Outros Documentos

Files in This Item:
File Description SizeFormat 
2-s2.0-84891515364.pdf279.94 kBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.