Learning wormholes for sparsely labelled clustering
Ong, EJ and Bowden, R (2006) Learning wormholes for sparsely labelled clustering In: 18th International Conference on Pattern Recognition (ICPR 2006), 2006-08-20 - 2006-08-24, Hong Kong, PEOPLES R CHINA.
icpr2006_Ong_Bowden.pdf - Accepted Version
Available under License : See the attached licence file.
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. The learning algorithms were then shown to successfully extract suitable distance functions in various clustering problems, ranging from synthetic 2D data to symbolic representations of unlabelled images
|Item Type:||Conference or Workshop Item (UNSPECIFIED)|
|Additional Information:||© 2006 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.|
|Divisions:||Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing|
|Depositing User:||Symplectic Elements|
|Date Deposited:||04 May 2012 15:32|
|Last Modified:||23 Sep 2013 19:24|
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