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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.
Rights: fechado
Identifier DOI: 10.1109/SIBGRAPI.2013.13
Date Issue: 2013
Appears in Collections:Unicamp - Artigos e Outros Documentos

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