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Semi-Supervised Extreme Learning Machine using L1-Graph

Volume 14, Number 4, April 2018, pp. 603-610
DOI: 10.23940/ijpe.18.04.p2.603610

Hongwei Zhaoa,b,c, Yang Liub, Shenglan Liua,b, and Lin Fenga,b

aSchool of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, China
bSchool of Innovation Experiment, Dalian University of Technology, Dalian, 116024, China
cInformation and Engineering College of Dalian University, Dalian, 116024, China

(Submitted on December 27, 2017; Revised on January 28, 2018; Accepted on February 24, 2018)


The semi-supervised learning method has been widely used in the field of pattern recognition. Semi-supervised Extreme Learning Machine (SELM) is a typical semi-supervised learning algorithm. The graph construction result of the sample data has a tremendous impact on the SELM algorithm. In traditional graph composition methods such as Laplace graph, LLE graph and K neighboring graph, neighborhood parameters are specified by humans. If there are noises or uneven distribution in the data, the results are not very good. This paper proposes a SELM algorithm based on L1-Graph, which features no specifying parameters, is robust against noise, has a sparse solution and so on. The experiment confirms the effectiveness of the method.


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