Semi-supervised extreme learning machine using L1-graph

被引：0
|
作者：
Zhao H. [1 ,2 ,3 ]
Liu Y. [2 ]
Liu S. [1 ,2 ]
Feng L. [1 ,2 ]
机构：
[1] School of Computer Science and Technology, Dalian University of Technology, Dalian
[2] School of Innovation Experiment, Dalian University of Technology, Dalian
[3] Information and Engineering College of Dalian University, Dalian
来源：
Feng, Lin (fenglin@dlut.edu.cn) | 2018年 / Totem Publishers Ltd卷 / 14期
关键词：
L1-Graph; SELM; Semi-supervised learning; Sparse representation;
D O I：
10.23940/ijpe.18.04.p2.603610
中图分类号：
学科分类号：
摘要：
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. © 2018 Totem Publisher, Inc. All rights reserved.
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页码：603 / 610
页数：7
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