Semi-Supervised Extreme Learning Machine using L1-Graph

doi:10.23940/ijpe.18.04.p2.603610

Int J Performability Eng ›› 2018, Vol. 14 ›› Issue (4): 603-610.doi: 10.23940/ijpe.18.04.p2.603610

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Semi-Supervised Extreme Learning Machine using L1-Graph

Hongwei Zhao^{a, b, c}, Yang Liu^b, Shenglan Liu^{a, b}, and Lin Feng^{a, 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

Abstract

Abstract:

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.

Submitted on December 27, 2017; Revised on January 28, 2018; Accepted on February 24, 2018
References: 9

Hongwei Zhao, Yang Liu, Shenglan Liu, and Lin Feng. Semi-Supervised Extreme Learning Machine using L1-Graph [J]. Int J Performability Eng, 2018, 14(4): 603-610.

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Semi-Supervised Extreme Learning Machine using L1-Graph

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