Self-taught learning via exponential family sparse coding for cost-effective patient thought record categorization
Hua Wang, Heng Huang, Monica Basco, Molly Lopez, Fillia Makedon
Personal and Ubiquitous Computing - 2012
Automatic patient thought record categorization (TR) is important in cognitive behavior therapy, which is an useful augmentation of standard clinic treatment for major depressive disorder. Because both collecting and labeling TR data are expensive, it is usually cost prohibitive to require a large amount of TR data, as well as their corresponding category labels, to train a classification model with high classification accuracy. Because in practice we only have very limited amount of labeled and unlabeled training TR data, traditional semi-supervised learning methods and transfer learning methods, which are the most commonly used strategies to deal with the lack of training data in statistical learning, cannot work well in the task of automatic TR categorization. To address this challenge, we propose to tackle the TR categorization problem from a new perspective via self-taught learning, an emerging technique in machine
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Cite this paper
MLA
Wang, Hua, et al. "Self-taught learning via exponential family sparse coding for cost-effective patient thought record categorization." Personal and ubiquitous computing 18.1 (2014): 27-35.
BibTeX
@article{wang2014self,
title={Self-taught learning via exponential family sparse coding for cost-effective patient thought record categorization},
author={Wang, Hua and Huang, Heng and Basco, Monica and Lopez, Molly and Makedon, Fillia},
journal={Personal and ubiquitous computing},
volume={18},
number={1},
pages={27--35},
year={2014},
publisher={Springer-Verlag}
}