Name
Abstract | ||
|---|---|---|
Nonlinear dimensionality reduction methods have demonstrated top-notch performance in many pattern recognition and image classification tasks. Despite their popularity, they suffer from highly expensive time and memory requirements, which render them inapplicable to large-scale datasets. To leverage such cases we propose a new method called “Path-Based Isomap”. Similar to Isomap, we exploit geodes... |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/TPAMI.2015.2487981 | IEEE Transactions on Pattern Analysis and Machine Intelligence |
Keywords | Field | DocType |
Manifolds,Optimization,Complexity theory,Principal component analysis,Approximation methods,Approximation algorithms,Estimation | Data point,Computer vision,Approximation algorithm,Embedding,Pattern recognition,Computer science,Artificial intelligence,Nonlinear dimensionality reduction,Contextual image classification,Principal component analysis,Geodesic,Isomap | Journal |
Volume | Issue | ISSN |
38 | 7 | 0162-8828 |
Citations | PageRank | References |
5 | 0.42 | 21 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Amir Najafi | 1 | 5 | 0.76 |
| Amir Joudaki | 2 | 5 | 0.42 |
| Emad Fatemizadeh | 3 | 117 | 13.86 |