Subspace clustering using affinity propagation

• We study the problem of subspace clustering.
• We propose an algorithm by combining affinity propagation and attribute weighting.
• The algorithm does not su er from the cluster center initialization problem.
• Experiments on synthetic and real data show that the algorithm performs well.

This paper proposes a subspace clustering algorithm by introducing attribute weights in the affinity propagation algorithm. A new step is introduced to the affinity propagation process to iteratively update the attribute weights based on the current partition of the data. The relative magnitude of the attribute weights can be used to identify the subspaces in which clusters are embedded. Experiments on both synthetic data and real data show that the new algorithm outperforms the affinity propagation algorithm in recovering clusters from data.