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Invita: DEPARTAMENTO DE CIENCIAS DE LA COMPUTACIÓN
Ponente(s): Richard Connor
Resumen: This work derives from the domain of similarity search: the apparently simple task of finding objects, from some large collection, which are similar to another object presented as a query. Everyday examples exist, such as Shazam and Google Image Search. The general problem is not solved however, as even a relatively modest collection of eg 1M images may take ten days to query unless either huge parallel compute power, or clever methods, are applied. For clever methods, the step to the analysis of high-dimensional distance geometry is a clear requirement!
This talk describes the nSimplex method, where n objects in a search space are used to construct a geometric model in (n-1)-dimensional Euclidean space. This model gives strong upper- and lower-bound properties with respect to the original space. In conjunction with a further study of the distribution of angles in high-dimensional spaces, we then use the nSimplex method to produce a distance estimator in a Euclidean space of much lower dimension than the original.
We consider this estimation function used as a general dimensionality reduction mechanism, and compare it with the mainstream techniques of Principle Component Analysis, Multidimensional Scaling, and Random Projection. It performs better than these techniques in almost all circumstances and, unlike these methods, can also be applied to some useful non-Euclidean spaces.
Bio
Prof. Richard Connor graduated with BSc and PhD from the University o

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