LTPI: A Spectral Clustering Method Based on Local Topology Preserving Indexing and Its Application for Document Clustering

In terms of machine learning theory, the intrinsic geometrical structure of the original data space is usually embedded in the low-dimensional manifold. The extraction of optimized manifold features could improve the performance of clustering. This paper presents a new spectral clustering method called local topology preserving indexing (LTPI). In this algorithm, the data are projected into a low-dimensional feature space in which the distances between the data points in the same local patches are minimized and the distances from the data points outside these patches are maximized simultaneously. The proposed LTPI method can effectively discover the intrinsic local topologies embedded in original high-dimensional space. The comparison experiments for document clustering demonstrate its effectiveness.