Robust and Discriminative Self-Taught Learning
Hua Wang, Feiping Nie, Heng Huang
ICML - 2013
The lack of training data is a common challenge in many machine learning problems, which is often tackled by semi-supervised learning methods or transfer learning methods. The former requires unlabeled images from the same distribution as the labeled ones and the latter leverages labeled images from related homogenous tasks. However, these restrictions often cannot be satisfied. To address this, we propose a novel robust and discriminative self-taught learning approach to utilize any unlabeled data without the above restrictions. Our new approach employs a robust loss function to learn the dictionary, and enforces the structured sparse regularization to automatically select the optimal dictionary basis vectors and incorporate the supervision information contained in the labeled data. We derive an efficient iterative algorithm to solve the optimization problem and rigorously prove its convergence. Promising results in extensive experiments have validated the proposed approach.
Links
- View publications from Hua Wang
- View publications presented in ICML
- View publications researching Dictionary Learning
- View publications researching Robust Learning Models
- View publications researching Transfer Learning
- View publications applied to Computer Vision
Cite this paper
MLA
Wang, Hua, Feiping Nie, and Heng Huang. "Robust and discriminative self-taught learning." International conference on machine learning. 2013.
BibTeX
@inproceedings{wang2013robust,
title={Robust and discriminative self-taught learning},
author={Wang, Hua and Nie, Feiping and Huang, Heng},
booktitle={International conference on machine learning},
pages={298--306},
year={2013}
}