We propose a new method for supervised learning, the “
Nonlinear feature selection using sparsity-promoted centroid-encoder
The contribution of our work is two-fold. First, we propose a novel feature selection technique, sparsity-promoted centroid-encoder (SCE). The model uses the nonlinear mapping of artificial neural networks to reconstruct a sample as its class centroid and, at the same time, apply a
- PAR ID:
- 10468830
- Publisher / Repository:
- Springer Science + Business Media
- Date Published:
- Journal Name:
- Neural Computing and Applications
- Volume:
- 35
- Issue:
- 29
- ISSN:
- 0941-0643
- Format(s):
- Medium: X Size: p. 21883-21902
- Size(s):
- p. 21883-21902
- Sponsoring Org:
- National Science Foundation
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