Spectral Clustering with Local Projection Distance Measurement

Abstract

Constructing a rational affinity matrix is crucial for spectral clustering. In this paper, a novel spectral clustering via local projection distance measure (LPDM) is proposed. In this method, the Local-Projection-Neighborhood (LPN) is defined, which is a region between a pair of data, and other data in the LPN are projected onto the straight line among the data pairs. Utilizing the Euclidean distance between projective points, the local spatial structure of data can be well detected to measure the similarity of objects. Then the affinity matrix can be obtained by using a new similarity measurement, which can squeeze or widen the projective distance with the different spatial structure of data. Experimental results show that the LPDM algorithm can obtain desirable results with high performance on synthetic datasets, real-world datasets, and images.