LMAE: A large margin Auto-Encoders for classification.

	LMAE: A large margin Auto-Encoders for classification.
	Liu, Weifeng; Ma, Tengzhou; Xie, Qiangsheng; Tao, Dapeng; Cheng, Jun
刊名	SIGNAL PROCESSING
	2017
文献子类	期刊论文
英文摘要	Auto-Encoders, as one representative deep learning method, has demonstrated to achieve superior performance in many applications. Hence, it is drawing more and more attentions and variants of Auto Encoders have been reported including Contractive Auto-Encoders, Denoising Auto-Encoders, Sparse Auto Encoders and Nonnegativity Constraints Auto-Encoders. Recently, a Discriminative Auto-Encoders is reported to improve the performance by considering the within class and between class information. In this paper, we propose the Large Margin Auto-Encoders (LMAE) to further boost the discriminability by enforcing different class samples to be large marginally distributed in hidden feature space. Particularly, we stack the single-layer LMAE to construct a deep neural network to learn proper features. And finally we put these features into a softmax classifier for classification. Extensive experiments are conducted on the MNIST dataset and the CIFAR-10 dataset for classification respectively. The experimental results demonstrate that the proposed LMAE outperforms the traditional Auto-Encoders algorithm.
URL标识	查看原文
语种	英语
内容类型	期刊论文
源URL	[http://ir.siat.ac.cn:8080/handle/172644/11644]
专题	深圳先进技术研究院_集成所
作者单位	SIGNAL PROCESSING
推荐引用方式 GB/T 7714	Liu, Weifeng,Ma, Tengzhou,Xie, Qiangsheng,et al. LMAE: A large margin Auto-Encoders for classification.[J]. SIGNAL PROCESSING,2017.
APA	Liu, Weifeng,Ma, Tengzhou,Xie, Qiangsheng,Tao, Dapeng,&Cheng, Jun.(2017).LMAE: A large margin Auto-Encoders for classification..SIGNAL PROCESSING.
MLA	Liu, Weifeng,et al."LMAE: A large margin Auto-Encoders for classification.".SIGNAL PROCESSING (2017).