SPARSE REPRESENTATION-BASED ARCHETYPAL GRAPHS FOR SPECTRAL CLUSTERING

被引：0

作者：

Roscher, Ribana ^{[1
]}

Drees, Lukas ^{[1
]}

Wenzel, Susanne ^{[1
]}

机构：

[1] Univ Bonn, Inst Geodesy & Geoinformat, Bonn, Germany

来源：

2017 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) | 2017年

关键词：

Sparse representation; spectral clustering; sparse graphs; anomaly detection; change detection;

D O I：

暂无

中图分类号：

P [天文学、地球科学];

学科分类号：

07 ;

摘要：

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, socalled 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 kmeans 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.

引用

页码：2203 / 2206

页数：4