44,490 research outputs found
A geometric framework for modelling similarity search
The aim of this paper is to propose a geometric framework for modelling
similarity search in large and multidimensional data spaces of general nature,
which seems to be flexible enough to address such issues as analysis of
complexity, indexability, and the `curse of dimensionality.' Such a framework
is provided by the concept of the so-called similarity workload, which is a
probability metric space (query domain) with a distinguished finite
subspace (dataset), together with an assembly of concepts, techniques, and
results from metric geometry. They include such notions as metric transform,
\e-entropy, and the phenomenon of concentration of measure on
high-dimensional structures. In particular, we discuss the relevance of the
latter to understanding the curse of dimensionality. As some of those concepts
and techniques are being currently reinvented by the database community, it
seems desirable to try and bridge the gap between database research and the
relevant work already done in geometry and analysis.Comment: 11 pages, LaTeX 2.
Modelling word meaning using efficient tensor representations
Models of word meaning, built from a corpus of text, have demonstrated success in emulating human performance on a number of cognitive tasks. Many of these models use geometric representations of words to store semantic associations between words. Often word order information is not captured in these models. The lack of structural information used by these models has been raised as a weakness when performing cognitive tasks. This paper presents an efficient tensor based approach to modelling word meaning that builds on recent attempts to encode word order information, while providing flexible methods for extracting task specific semantic information
Statistical inference framework for source detection of contagion processes on arbitrary network structures
In this paper we introduce a statistical inference framework for estimating
the contagion source from a partially observed contagion spreading process on
an arbitrary network structure. The framework is based on a maximum likelihood
estimation of a partial epidemic realization and involves large scale
simulation of contagion spreading processes from the set of potential source
locations. We present a number of different likelihood estimators that are used
to determine the conditional probabilities associated to observing partial
epidemic realization with particular source location candidates. This
statistical inference framework is also applicable for arbitrary compartment
contagion spreading processes on networks. We compare estimation accuracy of
these approaches in a number of computational experiments performed with the
SIR (susceptible-infected-recovered), SI (susceptible-infected) and ISS
(ignorant-spreading-stifler) contagion spreading models on synthetic and
real-world complex networks
A statistical shape model for deformable surface
This short paper presents a deformable surface registration scheme which is based on the statistical shape
modelling technique. The method consists of two major processing stages, model building and model
fitting. A statistical shape model is first built using a set of training data. Then the model is deformed and
matched to the new data by a modified iterative closest point (ICP) registration process. The proposed
method is tested on real 3-D facial data from BU-3DFE database. It is shown that proposed method can
achieve a reasonable result on surface registration, and can be used for patient position monitoring in
radiation therapy and potentially can be used for monitoring of the radiation therapy progress for head and
neck patients by analysis of facial articulation
- …