Name
Abstract | ||
|---|---|---|
Previous work on image clustering focused on seeking a low dimensional structure from the high-dimensional image data by a shallow linear model, such as sparse subspace clustering (SSC) or low-rank representation (LRR). The recent advance of deep learning shows its superiority via handling data with nonlinear structure, i.e., sparse auto-encoder and independent subspace analysis(ISA), etc. However, most of this type of methods may ignore lots of useful information embedded in the original data. To this end, we propose a novel unsupervised learning algorithm via ISA incorporating the subspace structure within data. Specifically, we adopt the ISA to learn local translation invariant feature from data and integrate a prior subspace information into the output of the network simultaneously. This method performs an impressive powerful ability to learn the nature of data. By evaluating on public databases, CMU-PIE and ORL, the experimental results show that the proposed approach achieves better clustering results compared with the state-of-the-art ones. |
Year | Venue | Keywords |
|---|---|---|
2017 | 2017 24TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) | Subspace clustering, Independent subspace analysis, Prior, Sparse representation |
Field | DocType | ISSN |
Kernel (linear algebra),Data modeling,Subspace topology,Pattern recognition,Task analysis,Linear model,Random subspace method,Computer science,Artificial intelligence,Deep learning,Cluster analysis | Conference | 1522-4880 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chunchen Su | 1 | 0 | 0.34 |
| Zongze Wu | 2 | 65 | 11.45 |
| Ming Yin | 3 | 202 | 10.61 |
| Kaixin Li | 4 | 0 | 0.34 |
| Weijun Sun | 5 | 11 | 2.81 |