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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Peihao Huang Yan Huang Wei Wang Liang Wang |
| Copyright Year | 2014 |
| Description | Author affiliation: Center for Res. on Intell. Perception & Comput. (CRIPAC), Inst. of Autom., Beijing, China (Peihao Huang; Yan Huang; Wei Wang; Liang Wang) |
| Abstract | Clustering is a fundamental technique widely used for exploring the inherent data structure in pattern recognition and machine learning. Most of the existing methods focus on modeling the similarity/dissimilarity relationship among instances, such as k-means and spectral clustering, and ignore to extract more effective representation for clustering. In this paper, we propose a deep embedding network for representation learning, which is more beneficial for clustering by considering two constraints on learned representations. We first utilize a deep auto encoder to learn the reduced representations from the raw data. To make the learned representations suitable for clustering, we first impose a locality-persevering constraint on the learned representations, which aims to embed original data into its underlying manifold space. Then, different from spectral clustering which extracts representations from the block diagonal similarity matrix, we apply a group sparsity constraint for the learned representations, and aim to learn block diagonal representations in which the nonzero groups correspond to its cluster. After obtaining the learned representations, we use k-means to cluster them. To evaluate the proposed deep embedding network, we compare its performance with k-means and spectral clustering on three commonly-used datasets. The experiments demonstrate that the proposed method achieves promising performance. |
| Starting Page | 1532 |
| Ending Page | 1537 |
| File Size | 222184 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781479952090 |
| ISSN | 10514651 |
| DOI | 10.1109/ICPR.2014.272 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-08-24 |
| Publisher Place | Sweden |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Neural networks Clustering methods Image reconstruction Clustering algorithms Linear programming Manifolds |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition |
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