Reference TypeConference Proceedings
Citation KeyEscalanteTaubNonaGold:2013:UsUnLe
Author1 Escalante, Diego Alonso Chávez
2 Taubin, Gabriel
3 Nonato, Luis Gustavo
4 Goldenstein, Siome Klein
Affiliation1 IC-UNICAMP
2 School of Engineering, Brown University
TitleUsing Unsupervised Learning for Graph Construction in Semi-Supervised Learning with Graphs
Conference NameConference on Graphics, Patterns and Images, 26 (SIBGRAPI)
EditorBoyer, Kim
Hirata, Nina
Nedel, Luciana
Silva, Claudio
Book TitleProceedings
DateAug. 5-8, 2013
Publisher CityLos Alamitos
PublisherIEEE Computer Society
Conference LocationArequipa, Peru
KeywordsSemi-Supervised Learning, Growing Neural Gas.
AbstractSemi-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.
Tertiary TypeFull Paper
Size542 KiB
Number of Files1
Target File114517.pdf
Last Update2013:
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