Name
Abstract | ||
|---|---|---|
Autoencoding is a vital branch of representation learning in deep neural networks (DNNs). The extreme learning machine-based autoencoder (ELM-AE) has been recently developed and has gained popularity for its fast learning speed and ease of implementation. However, the ELM-AE uses random hidden node parameters without tuning, which may generate meaningless encoded features. In this brief, we first propose a within-class scatter information constraint-based AE (WSI-AE) that minimizes both the reconstruction error and the within-class scatter of the encoded features. We then build stacked WSI-AEs into a one-class classification (OCC) algorithm based on the hierarchical regularized least-squared method. The effectiveness of our approach was experimentally demonstrated in comparisons with several state-of-the-art AEs and OCC algorithms. The evaluations were performed on several benchmark data sets. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TNNLS.2020.3015860 | IEEE Transactions on Neural Networks and Learning Systems |
Keywords | DocType | Volume |
Autoencoder (AE),extreme learning machine (ELM),one-class classification (OCC),scatter matrix | Journal | 32 |
Issue | ISSN | Citations |
8 | 2162-237X | 3 |
PageRank | References | Authors |
0.38 | 12 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tianlei Wang | 1 | 34 | 9.77 |
| Jiuwen Cao | 2 | 369 | 19.44 |
| Xiaoping Lai | 3 | 240 | 25.14 |
| Gaurav Bhatnagar | 4 | 524 | 41.09 |