CASIA OpenIR  > 复杂系统管理与控制国家重点实验室  > 深度强化学习
Image Clustering based on Deep Sparse Representations
Lv Le1; Zhao Dongbin1; Deng QingQiong2
Conference NameThe 2016 IEEE Symposium Series on Computational Intelligence
Conference Date6-9 Dec. 2016
Conference PlaceAthens, Greece
AbstractCurrently, the supervised trained deep neural networks (DNNs) have been successfully applied in several image classification tasks. However, how to extract powerful data representations and discover semantic concepts from unlabeled data is a more practical issue. Unsupervised feature learning methods aim at extracting abstract representations from unlabeled data. Large amount of research works illustrate that these representations can be directly used in the supervised tasks. However, due to the high dimensionality of these representations, it is difficult to discover the categorical concepts among them in an unsupervised way. In this paper, we propose combining the winner-take-all autoencoder with the bipartite graph partitioning algorithm to cluster unlabeled image data. The winner-take-all autoencoder can learn the additive sparse representations. By the experiments, we present the properties of the sparse representations. The bipartite graph partitioning can take full advantage of them and generate semantic clusters. We discover that the confident instances in each cluster are well discriminated. Based on the initial clustering result, we further train a support vector machine (SVM) to refine the clusters. Our method can discover the categorical concepts rapidly and the experiment shows that the clustering performance of our method is good.
Citation statistics
Document Type会议论文
Affiliation1.The State Key Laboratory of Management and Control for Complex Systems Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
2.College of Information Science and Technology, Beijing Normal University, Beijing, 100875, China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Lv Le,Zhao Dongbin,Deng QingQiong. Image Clustering based on Deep Sparse Representations[C],2017.
Files in This Item: Download All
File Name/Size DocType Version Access License
07850110.pdf(410KB)会议论文 开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Lv Le]'s Articles
[Zhao Dongbin]'s Articles
[Deng QingQiong]'s Articles
Baidu academic
Similar articles in Baidu academic
[Lv Le]'s Articles
[Zhao Dongbin]'s Articles
[Deng QingQiong]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Lv Le]'s Articles
[Zhao Dongbin]'s Articles
[Deng QingQiong]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: 07850110.pdf
Format: Adobe PDF
This file does not support browsing at this time
All comments (0)
No comment.

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.