Learning Wormholes for Sparsely Labelled Clustering

Distance functions are an important component in many learning applications. However, the correct function is context dependent, therefore it is advantageous to learn a distance function using available training data. Many existing distance functions is the requirement for data to exist in a space of constant dimensionality and not possible to be directly used on symbolic data. To address these problems, this paper introduces an alternative learnable distance function, based on multi-kernel distance bases or "wormholes that connects spaces belonging to similar examples that were originally far away close together. This work only assumes the availability of a set data in the form of relative comparisons, avoiding the need for having labelled or quantitative information. To learn the distance function, two algorithms were proposed: 1) Building a set of basic wormhole bases using a Boosting-inspired algorithm. 2) Merging different distance bases together for better generalisation. T...

• Type: Conference
• Year: 2006
• Where: ICPR
• Authors: Eng-Jon Ong, Richard Bowden
• Keywords: Computer Vision, Distance Function, ICPR 2006, Learnable Distance Function, Multi-kernel Distance Bases