Spectral Embedded Clustering on Multi-Manifold

Due to the incorporation of dimensionality reduction, spectral clustering (SC) based methods have a unique advantage in dealing with high-dimensional data. However, data is mainly characterized by its distribution on multiple low-dimensional manifolds, which is ignored by some SC-based methods. We put forward a new spectral multi-manifold embedded clustering (SMEC) method in this paper, which incorporates the local geometric information of data into the traditional SC. Thus, the designed similarity matrix in SMEC is able to capture both the local and global discriminating information, which results in improved clustering. Experimental results on seven benchmark datasets demonstrate our proposed method's promising performance.