PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Semi-supervised visual clustering for spherical coordinates systems
Boris Chidlovskii and Loic Lecerf
In: ACM SAC 2008, MArch 16-20, 2008, Fortaleza, Brazil.


In this paper we propose a method that combines the advanced data analysis of the automatic statistical methods and the flexibility and manual parameter tuning of interactive visual clustering. We present the {\it Semi-Supervised Visual Clustering} (SSVC) interface; its main contribution is the learning of the optimal projection distance metric for the {\it star and spherical coordinate} visualization systems. Beyond the convent ional manual setting, it couples the visual clustering with the automatic setting where the projection distance metric is learned from the available set of user feedbacks in the form of either item similarities or direct item annotations. Moreover, SSVC interface allows for the {\it hybrid} setting where some parameters are manually set by the user while the remaining parameters are determined by the optimal distance algorithm.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:5326
Deposited By:Boris Chidlovskii
Deposited On:24 March 2009