%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi/2013/07.12.22.54
%2 sid.inpe.br/sibgrapi/2013/07.12.22.54.34
%@doi 10.1109/SIBGRAPI.2013.13
%T Using Unsupervised Learning for Graph Construction in Semi-Supervised Learning with Graphs
%D 2013
%A Escalante, Diego Alonso Chávez,
%A Taubin, Gabriel,
%A Nonato, Luis Gustavo,
%A Goldenstein, Siome Klein,
%@affiliation IC-UNICAMP
%@affiliation School of Engineering, Brown University
%@affiliation ICMC-USP
%@affiliation IC-UNICAMP
%E Boyer, Kim,
%E Hirata, Nina,
%E Nedel, Luciana,
%E Silva, Claudio,
%B Conference on Graphics, Patterns and Images, 26 (SIBGRAPI)
%C Arequipa, Peru
%8 5-8 Aug. 2013
%I IEEE Computer Society
%J Los Alamitos
%S Proceedings
%K Semi-Supervised Learning, Growing Neural Gas.
%X 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.
%@language en
%3 114517.pdf