Learning spatially localized, parts-based representation
In this paper, we propose a novel method, called local non-negative matrix factorization (LNMF), for learning spatially localized, parts-based subspace representation of visual ...
Publications
1 shownFrequent Co-Authors
Researcher Info
- h-index
- 1
- Publications
- 1
- Citations
- 780
- Institution
- Peking University
External Links
Identifiers
- ORCID
- 0009-0001-3112-4810
Impact Metrics
h-index
1
h-index: Number of publications with at least h citations each.