Semi-supervised Feature Selection Based on Label Propagation and Subset Selection

View Researcher II's Other Codes

Disclaimer: “The provided code links for this paper are external links. Science Nest has no responsibility for the accuracy, legality or content of these links. Also, by downloading this code(s), you agree to comply with the terms of use as set out by the author(s) of the code(s).”

Please contact us in case of a broken link from here

Authors Yun Liu, Feiping Nie, Jigang Wu, Lihui Chen
Journal/Conference Name The International Conference on Computer and Information Application (ICCIA)
Paper Category
Paper Abstract In practice, the data to be handled are often high dimensional, and labeled data are often very limited while a large numbers of unlabeled data can be easily collected. Feature selection is an important method to deal with high dimensional data. In this paper, we propose a novel semi-supervised feature selection algorithm to select relevant features using both labeled and unlabeled data. Specifically, the algorithm explores the distribution of the labeled and unlabeled data with a special label propagation method to obtain the soft labels of unlabeled data, then an efficient algorithm to optimize the trace ratio criterion is used to directly select the optimal feature subset. Experimental results verify the effectiveness of the proposed algorithm, and show significant improvement over traditional supervised feature selection algorithms.
Date of publication 2010
Code Programming Language MATLAB

Copyright Researcher II 2022