.png){kind=icon}
返回
Supervised Extreme Learning Machine-Based Auto-Encoder for Discriminative Feature Learning
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2019.2962067.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper, a Supervised Extreme Learning Machine-based Auto-Encoder (SELM-AE) is proposed for discriminative Feature Learning. Different from traditional ELM-AE (designed based on data information X only), SELM-AE is designed based on both data information X and label information T. In detail, SELM-AE not only minimizes the reconstruction error of input data but also minimizes the intraclass distance and maximizes the inter-class distance in the new feature space. Under this way, the new data representation extracted by proposed SELM-AE is more discriminative than traditional ELM-AE for further classification. Then multiple SELM-AEs are stacked layer by layer to develop a new multi-layer perceptron (MLP) network called ML-SAE-ELM. Benefit from SELM-AE, the proposed ML-SAE-ELM is highly effective on classification than ELM-AE based MLP. Moreover, different from ELM-AE based MLP that requires large number of hidden nodes to achieve satisfactory accuracy, ML-SAE-ELM usually takes very small number of hidden nodes on both feature learning and classification stages to achieve better accuracy, which highly lightens the network memory requirement. The proposed method has been evaluated over 13 benchmark binary and multi-class datasets and one complicated image dataset. As shown in the experimental results, through the visualization of data representation, the proposed SELM-AE extracts more discriminative data representation than ELM-AE. Moreover, the shallow ML-SAE-ELM with smaller hidden nodes achieves higher classification accuracy than hierarchical ELM (a commonly used effective ELM-AE based MLP) on most evaluated datasets.
Keyword:
Extreme learning machine
ELM based auto-encoder
supervised ELM-AE
discriminative data representation
multi-layer perceptron
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
Productivity enhancement of solar still by PCM and Nanoparticles miscellaneous basin absorbing materials.png)
DesalinationOAAI
DesalinationIF0
OAAI分享收藏
A Two-Stage Neural Network for Sleep Stage Classification Based on Feature Learning, Sequence Learning and Data AugmentationIEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAI分享收藏Stacked Denoising Extreme Learning Machine Autoencoder Based on Graph Embedding for Feature Representation基于图嵌入特征表示的堆叠去噪极限学习机自动编码器IEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAI分享收藏分享收藏Graphene/Ionic Liquid Binary Electrode Material for High Performance Supercapacitor用于高性能超级电容器的石墨烯/离子液体二元电极材料
PREAI
分享收藏
查看更多内容