@inproceedings{ef7a7b9ee23749d0af715dd41d05a043,
title = "Noise-robust semi-supervised learning by large-scale sparse coding",
abstract = "This paper presents a large-scale sparse coding algorithm to deal with the challenging problem of noiserobust semi-supervised learning over very large data with only few noisy initial labels. By giving an L1-norm formulation of Laplacian regularization directly based upon the manifold structure of the data, we transform noise-robust semi-supervised learning into a generalized sparse coding problem so that noise reduction can be imposed upon the noisy initial labels. Furthermore, to keep the scalability of noise-robust semi-supervised learning over very large data, we make use of both nonlinear approximation and dimension reduction techniques to solve this generalized sparse coding problem in linear time and space complexity. Finally, we evaluate the proposed algorithm in the challenging task of large-scale semi-supervised image classification with only few noisy initial labels. The experimental results on several benchmark image datasets show the promising performance of the proposed algorithm.",
author = "Zhiwu Lu and Xin Gao and Liwei Wang and Wen, {Ji Rong} and Songfang Huang",
note = "Publisher Copyright: Copyright {\textcopyright} 2015, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.; 29th AAAI Conference on Artificial Intelligence, AAAI 2015 and the 27th Innovative Applications of Artificial Intelligence Conference, IAAI 2015 ; Conference date: 25-01-2015 Through 30-01-2015",
year = "2015",
month = jun,
day = "1",
language = "English (US)",
series = "Proceedings of the National Conference on Artificial Intelligence",
publisher = "AI Access Foundation",
pages = "2828--2834",
booktitle = "Proceedings of the 29th AAAI Conference on Artificial Intelligence, AAAI 2015 and the 27th Innovative Applications of Artificial Intelligence Conference, IAAI 2015",
address = "United States",
}