@inproceedings{a73d6a86dff54292ad5f6358382f5eeb,
title = "Sparse representation-based archetypal graphs for spectral clustering",
abstract = "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.",
keywords = "Sparse representation, anomaly detection, change detection, sparse graphs, spectral clustering",
author = "Ribana Roscher and Lukas and Drees and Susanne Wenzel",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 37th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2017 ; Conference date: 23-07-2017 Through 28-07-2017",
year = "2017",
month = dec,
day = "1",
doi = "10.1109/IGARSS.2017.8127425",
language = "English",
series = "International Geoscience and Remote Sensing Symposium (IGARSS)",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2203--2206",
booktitle = "2017 IEEE International Geoscience and Remote Sensing Symposium",
}