کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4977467 | 1451926 | 2017 | 7 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
LMAE: A large margin Auto-Encoders for classification
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موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
پردازش سیگنال
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چکیده انگلیسی
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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Signal Processing - Volume 141, December 2017, Pages 137-143
Journal: Signal Processing - Volume 141, December 2017, Pages 137-143
نویسندگان
Liu Weifeng, Ma Tengzhou, Xie Qiangsheng, Tao Dapeng, Cheng Jun,