Networks construction for semi-supervised learning

• Grant number: 11/21880-3
• Support Opportunities: Scholarships in Brazil - Doctorate
• Start date: March 01, 2012
• End date: August 31, 2015
• Field of knowledge: Physical Sciences and Mathematics - Computer Science
• Principal Investigator: Alneu de Andrade Lopes
• Grantee: Lilian Berton
• Host Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil

Abstract

Databases are getting larger and, in many situations, only a small subset of data items can be labeled. This happens because the labeling process is often expensive, time consuming and require the involvement of human experts. Thus, the semi-supervised learning techniques, directed to situations which there is only a small fraction of labeled data, have become very relevant. Several semi-supervised algorithms have been proposed, showing that it is possible to obtain good results using prior knowledge. Among these algorithms, those based on graphs has gained prominence in the area. Such interest is justified by the benefits provided by the representation via graphs, such as the ability to capture the topological structure of the data, represent hierarchical structures (graphs and sub graphs), as well, to detect clusters. However, most available data is represented by an attribute-value table, making necessary the study of graph construction techniques to apply in such algorithms. This work focuses on semis-supervised algorithms based on graphs, which make use of a weight matrix between the vertices of a sparse graph, since denser graphs can degenerate the solution. As the generation of the weight matrix and the sparse graph, and their relation to the performance of the algorithms has been little investigated in the literature, this project aims to investigate these aspects and propose new methods of networks generation, covering the advantages of two or more methods of network construction from attribute-value data, or considering features not explored in the literature yet. The generated networks will be applied in semi-supervised learning algorithms that make use of graphs.

Scientific publications

(4)

(The scientific publications listed on this page originate from the Web of Science or SciELO databases. Their authors have cited FAPESP grant or fellowship project numbers awarded to Principal Investigators or Fellowship Recipients, whether or not they are among the authors. This information is collected automatically and retrieved directly from those bibliometric databases.)

BERTON, LILIAN; FALEIROS, THIAGO DE PAULO; VALEJO, ALAN; VALVERDE-REBAZA, JORGE; LOPES, ALNEU DE ANDRADE. RGCLI: Robust Graph that Considers Labeled Instances for Semi Supervised Learning. Neurocomputing, v. 226, p. 238-248, FEB 22 2017. (11/23689-9, 11/21880-3, 15/14228-9, 13/12191-5)

BERTON, LILIAN; LOPES, ALNEU DE ANDRADE; VEGA-OLIVEROS, DIDIER A.; IEEE. A Comparison of Graph Construction Methods for Semi-Supervised Learning. 2018 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), v. N/A, p. 8-pg., 2018-01-01. (16/23698-1, 15/14228-9, 11/21880-3)

BERTON, LILIAN; LOPES, ALNEU DE ANDRADE; YANG, Q; WOOLDRIDGE, M. Graph Construction for Semi-Supervised Learning. PROCEEDINGS OF THE TWENTY-FOURTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI), v. N/A, p. 2-pg., 2015-01-01. (11/21880-3)

ASSIRATI, L.; SILVA, N. R.; BERTON, L.; LOPES, A. A.; BRUNO, O. M.; VAGENAS, EC; VLACHOS, DS. Performing edge detection by Difference of Gaussians using q-Gaussian kernels. WAKE CONFERENCE 2021, v. 490, p. 4-pg., 2014-01-01. (11/21880-3, 11/21467-9, 11/23112-3)

Academic Publications

(References retrieved automatically from State of São Paulo Research Institutions)

BERTON, Lilian. Graph construction based on neighborhood for semisupervised. 2016. Doctoral Thesis - Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação (ICMC/SB) São Carlos.