Unsupervised and Semi-supervised Learning via L1-norm Graph
Feiping Nie, Hua Wang, Heng Huang, Chris Ding.
ICCV - 2011
In this paper, we propose a novel l1-norm graph model to perform unsupervised and semi-supervised learning methods. Instead of minimizing the l2-norm of spectral embedding as traditional graph based learning methods, our new graph learning model minimizes the l1-norm of spectral embedding with well motivation. The sparsity produced by the l1-norm minimization results in the solutions with much clearer cluster structures, which are suitable for both image clustering and classification tasks. We introduce a new efficient iterative algorithm to solve the l1-norm of spectral embedding minimization problem, and prove the convergence of the algorithm. More specifically, our algorithm adaptively re-weight the original weights of graph to discover clearer cluster structure. Experimental results on both toy data and real image data sets show the effectiveness and advantages of our proposed method.
Links
- View publications from Hua Wang
- View publications presented in ICCV
- View publications researching Embeddings
- View publications researching Graph Representations/Learning
- View publications researching Robust Learning Models
Cite this paper
MLA
Nie, Feiping, et al. "Unsupervised and semi-supervised learning via ℓ 1-norm graph." 2011 International Conference on Computer Vision. IEEE, 2011.
BibTeX
@inproceedings{nie2011unsupervised,
title={Unsupervised and semi-supervised learning via ℓ 1-norm graph},
author={Nie, Feiping and Wang, Hua and Huang, Heng and Ding, Chris},
booktitle={2011 International Conference on Computer Vision},
pages={2268--2273},
year={2011},
organization={IEEE}
}