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Image clustering based on the deep sparse representations
Le Lv1; Dongbin Zhao1; Qingqiong Deng2
Source PublicationComputational Intelligence (SSCI), 2016 IEEE Symposium Series on
Abstract     Currently, 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.
KeywordFeature Extraction
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
Le Lv,Dongbin Zhao,Qingqiong Deng. Image clustering based on the deep sparse representations[C],2016:1-6.
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