Semi-Supervised Extreme Learning Machine using L1-Graph
Hongwei Zhao · International Journal of Performability Engineering · 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.