Sparse Representation-Based Archetypal Graphs For Spectral Clustering

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.