Clustering with a Domain-Specific Distance Measure
Steven Gold, Eric Mjolsness, Anand Rangarajan · 1993
With a point matching distance measure which is invariant under translation, rotation and permutation, we learn 2-D point-set objects, by clustering noisy point-set images. Unlike traditional clustering methods which use distance measures that operate on feature vectors -- a representation common to most problem domains -- this object-based clustering technique employs a distance measure specific to a type of object within a problem domain. Formulating the clustering problem as two nested objective functions, we derive optimization dynamics similar to the Expectation-Maximization algorithm used in mixture models. 1 Introduction Clustering and related unsupervised learning techniques such as competitive learning and self-organizing maps have traditionally relied on measures of distance, like Euclidean or Mahalanobis distance, which are generic across most problem domains. Consequently, when working in complex domains like vision, extensive preprocessing is required to produce feature s...