Model card
ASNMF-SRP.
Zhong and Gaoclustering
Autoencoder-like Sparse Non-Negative Matrix Factorization with Structure Relationship Preservation. Integrates autoencoder principles into NMF with higher-order graph regularization.
§ 01 · Benchmarks
Every benchmark ASNMF-SRP has a recorded score for.
| # | Benchmark | Area · Task | Metric | Value | Rank | Date | Source |
|---|---|---|---|---|---|---|---|
| 01 | pendigits | Computer Vision · Optical Character Recognition | ari | 68.5% | #1 | 2025-08-19 | source ↗ |
| 02 | pendigits | Computer Vision · Optical Character Recognition | accuracy | 80.4% | #4 | 2025-08-19 | source ↗ |
| 03 | pendigits | Computer Vision · Optical Character Recognition | nmi | 80.1% | #4 | 2025-08-19 | source ↗ |
Rank column shows this model’s position vs all other models scored on the same benchmark + metric (competitors after the slash). #1 in red means current SOTA. Sorted by rank, then newest result.
§ 03 · Papers
1 paper with results for ASNMF-SRP.
- 2025-08-19· Computer Vision· 3 results
Autoencoder-like Sparse Non-Negative Matrix Factorization with Structure Relationship Preservation
Ling Zhong, Haiyan Gao
§ 05 · Sources & freshness
Where these numbers come from.
unknown
3
results
3 of 3 rows marked verified.