Skip to main content
Article thumbnail
Location of Repository

Incremental spectral clustering and its application to topological mapping

By Christoffer Valgren, Tom Duckett and Achim Lilienthal

Abstract

This paper presents a novel use of spectral clustering algorithms to support cases where the entries in the affinity matrix are costly to compute. The method is incremental – the\ud spectral clustering algorithm is applied to the affinity matrix after each row/column is added – which makes it possible to inspect the clusters as new data points are added. The method is well suited to the problem of appearance-based, on-line topological mapping for mobile robots. In this problem domain, we show that we can reduce environment-dependent parameters of the clustering algorithm to just a single, intuitive parameter. Experimental results in large outdoor and indoor environments\ud show that we can close loops correctly by computing only a fraction of the entries in the affinity matrix. The accompanying video clip shows how an example map is produced by the\ud algorithm

Topics: G760 Machine Learning, H671 Robotics, G740 Computer Vision
Year: 2007
OAI identifier: oai:eprints.lincoln.ac.uk:1685

Suggested articles

Citations

  1. (2003). A comparison of spectral clustering algorithms,”
  2. (2005). Functional grouping of genes using spectral clustering and gene ontology,” in doi
  3. (2005). Incremental robot mapping with fingerprints of places,” in doi
  4. (2001). On spectral clustering: Analysis and an algorithm,”
  5. (2005). Robust path-based spectral clustering with application to image segmentation,” in doi
  6. (2004). Spectral clustering for robust motion segmentation,” doi
  7. (2005). Spectral clustering of biological sequence data,” in
  8. (2000). Vision-based navigation and environmental representations with an omni-directional camera.” doi

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.