Fast convergence for spectral clustering

M. Aiello, F. Andreozzi, E. Catanzariti, F. Isgrò, M. Santoro

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Over the last years computer vision researchers have shown great interest for the so called spectral clustering, where the data are clustered analysing the first few eigenvectors (i.e., the ones relative to the first eigenvalues) of a the Laplacian matrix, derived directly from the data-set. Note that for the purpose of data clustering the eigenvectors need not to be determined accurately. When clustering (segmenting) images the dimension of this matrix is large (e.g., an image as small as 100 × 100 results in a 10000 × 10000 matrix), and standard diagonalisation algorithms such Lanczos, necessary for determining the eigenvectors, do require a certain number of iterations: typically in the order of √n step for n × n matrices, and may take some iterations for getting close to the solutions. Here we report the first attempt using a recent diagonalisation algorithm (named APL) borrowed from the nuclear physics literature, that, among other properties, has the main advantage of obtaining in a small number of iteration steps eigenvectors, that even if not accurate, are good enough for performing a reasonable segmentation. In this sense we talk of fast convergence of spectral clustering. The experimental results obtained support this claim, and open the way to further work exploiting further detail of the algorithm not included in this study.

Original languageEnglish
Title of host publicationProceedings - 14th International conference on Image Analysis and Processing, ICIAP 2007
Number of pages6
Publication statusPublished - 2007
Event14th Edition of the International Conference on Image Analysis and Processing, ICIAP 2007 - Modena, Italy
Duration: Sep 10 2007Sep 14 2007


Other14th Edition of the International Conference on Image Analysis and Processing, ICIAP 2007

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition


Dive into the research topics of 'Fast convergence for spectral clustering'. Together they form a unique fingerprint.

Cite this