Sparse representation-based archetypal graphs for spectral clustering
- Resource Type
- Conference
- Authors
- Roscher, R.; Drees, L.; Wenzel, S.
- Source
- 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) Geoscience and Remote Sensing Symposium (IGARSS), 2017 IEEE International. :2203-2206 Jul, 2017
- Subject
- Aerospace
Components, Circuits, Devices and Systems
Fields, Waves and Electromagnetics
Geoscience
Power, Energy and Industry Applications
Signal Processing and Analysis
Diseases
Sparse matrices
Hyperspectral imaging
Earth
Dictionaries
Sparse representation
spectral clustering
sparse graphs
anomaly detection
change detection
- Language
- ISSN
- 2153-7003
We propose sparse representation-based archetypal graphs as input to spectral clustering for anomaly and change detection. The graph consists of vertices defined by data samples and edges which weights are determines by sparse representation. Besides relationships between all data samples, the graph also encodes the relationship to extremal points, so-called archetypes, which leads to an easily interpretable clustering result. We compare our approach to k-means clustering performed on the original feature representation and to k-means clustering performed on the sparse representation activations. Experiments show that our approach is able to deliver accurate and interpretable results for anomaly and change detection.