Cross-covariance regularized autoencoders for nonredundant sparse feature representation
文献类型：期刊论文
| 作者 | Chen, Jie1,2; Wu, ZhongCheng2,3 ; Zhang, Jun2; Li, Fang2; Li, WenJing2; Wu, ZiHeng2
|
| 刊名 | NEUROCOMPUTING
 |
| 出版日期 | 2018-11-17 |
| 卷号 | 316页码:49-58 |
| 关键词 | Autoencoder Cross-covariance Deep learning Feature representation Receptive fields |
| ISSN号 | 0925-2312 |
| DOI | 10.1016/j.neucom.2018.07.050 |
| 通讯作者 | Chen, Jie(cj2016@mail.ustc.edu.cn) |
| 英文摘要 | We propose a new feature representation algorithm using cross-covariance in the context of deep learning. Existing feature representation algorithms based on the sparse autoencoder and nonnegativity-constrained autoencoder tend to produce duplicative encoding and decoding receptive fields, which leads to feature redundancy and overfitting. We propose using the cross-covariance to regularize the feature weight vector to construct a new objective function to eliminate feature redundancy and reduce overfitting. The results from the MNIST handwritten digits dataset, the NORB normalized-uniform dataset and the Yale face dataset indicate that relative to other algorithms based on the conventional sparse autoencoder and nonnegativity-constrained autoencoder, our method can effectively eliminate feature redundancy, extract more distinctive features, and improve sparsity and reconstruction quality. Furthermore, this method improves the image classification performance and reduces the overfitting of conventional networks without adding more computational time. (C) 2018 Elsevier B.V. All rights reserved. |
| WOS关键词 | FEATURE-EXTRACTION ; NEURAL-NETWORKS ; NONNEGATIVITY CONSTRAINTS ; DENOISING AUTOENCODERS ; DEEP ; RECOGNITION ; ALGORITHM |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000443971900006 |
| 出版者 | ELSEVIER SCIENCE BV |
| 源URL | [http://ir.hfcas.ac.cn:8080/handle/334002/38791]  |
| 专题 | 中国科学院合肥物质科学研究院 |
| 通讯作者 | Chen, Jie |
| 作者单位 | 1.Univ Sci & Technol China, Grad Sch Comp Appl Technol, Hefei, Anhui, Peoples R China 2.Chinese Acad Sci, Hefei Inst Phys Sci, Hefei, Anhui, Peoples R China 3.Univ Sci & Technol China, Hefei, Anhui, Peoples R China |
| 推荐引用方式 GB/T 7714 | Chen, Jie,Wu, ZhongCheng,Zhang, Jun,et al. Cross-covariance regularized autoencoders for nonredundant sparse feature representation[J]. NEUROCOMPUTING,2018,316:49-58. |
| APA | Chen, Jie,Wu, ZhongCheng,Zhang, Jun,Li, Fang,Li, WenJing,&Wu, ZiHeng.(2018).Cross-covariance regularized autoencoders for nonredundant sparse feature representation.NEUROCOMPUTING,316,49-58. |
| MLA | Chen, Jie,et al."Cross-covariance regularized autoencoders for nonredundant sparse feature representation".NEUROCOMPUTING 316(2018):49-58. |
入库方式： OAI收割
来源：合肥物质科学研究院
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。