Name
Abstract | ||
|---|---|---|
Transform learning is a relatively new analysis formulation for learning a basis to represent signals. This work incorporates the simplest subspace clustering formulation Locally Linear Manifold Clustering, into the transform learning formulation. The core idea is to perform the clustering task in a transformed domain instead of processing directly the raw samples. The transform analysis step and the clustering are not done piecemeal but are performed jointly through the formulation of a coupled minimization problem. Comparison with state-of-the-art deep learning-based clustering methods and popular subspace clustering techniques shows that our formulation improves upon them. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.23919/EUSIPCO.2018.8553061 | European Signal Processing Conference |
Keywords | Field | DocType |
subspace clustering,transform learning,alternating optimization | Minimization problem,Signal processing,Subspace clustering,Computer science,Algorithm,Feature extraction,Minification,Artificial intelligence,Deep learning,Cluster analysis,Manifold | Conference |
ISSN | Citations | PageRank |
2076-1465 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jyoti Maggu | 1 | 2 | 4.43 |
| A. Majumdar | 2 | 644 | 75.83 |
| Emilie Chouzenoux | 3 | 202 | 26.37 |