23,556 research outputs found
From Relational Data to Graphs: Inferring Significant Links using Generalized Hypergeometric Ensembles
The inference of network topologies from relational data is an important
problem in data analysis. Exemplary applications include the reconstruction of
social ties from data on human interactions, the inference of gene
co-expression networks from DNA microarray data, or the learning of semantic
relationships based on co-occurrences of words in documents. Solving these
problems requires techniques to infer significant links in noisy relational
data. In this short paper, we propose a new statistical modeling framework to
address this challenge. It builds on generalized hypergeometric ensembles, a
class of generative stochastic models that give rise to analytically tractable
probability spaces of directed, multi-edge graphs. We show how this framework
can be used to assess the significance of links in noisy relational data. We
illustrate our method in two data sets capturing spatio-temporal proximity
relations between actors in a social system. The results show that our
analytical framework provides a new approach to infer significant links from
relational data, with interesting perspectives for the mining of data on social
systems.Comment: 10 pages, 8 figures, accepted at SocInfo201
Generalised Decision Level Ensemble Method for Classifying Multi-media Data
In recent decades, multimedia data have been commonly generated and used in various domains, such as in healthcare and social media due to their ability of capturing rich information. But as they are unstructured and separated, how to fuse and integrate multimedia datasets and then learn from them eectively have been a main challenge to machine learning. We present a novel generalised decision level ensemble method (GDLEM) that combines the multimedia datasets at decision level. After extracting features from each of multimedia datasets separately, the method trains models independently on each media dataset and then employs a generalised selection function to choose the appropriate models to construct a heterogeneous ensemble. The selection function is dened as a weighted combination of two criteria: the accuracy of individual models and the diversity among the models. The framework is tested on multimedia data and compared with other heterogeneous ensembles. The results show that the GDLEM is more exible and eective
Combining Multiple Clusterings via Crowd Agreement Estimation and Multi-Granularity Link Analysis
The clustering ensemble technique aims to combine multiple clusterings into a
probably better and more robust clustering and has been receiving an increasing
attention in recent years. There are mainly two aspects of limitations in the
existing clustering ensemble approaches. Firstly, many approaches lack the
ability to weight the base clusterings without access to the original data and
can be affected significantly by the low-quality, or even ill clusterings.
Secondly, they generally focus on the instance level or cluster level in the
ensemble system and fail to integrate multi-granularity cues into a unified
model. To address these two limitations, this paper proposes to solve the
clustering ensemble problem via crowd agreement estimation and
multi-granularity link analysis. We present the normalized crowd agreement
index (NCAI) to evaluate the quality of base clusterings in an unsupervised
manner and thus weight the base clusterings in accordance with their clustering
validity. To explore the relationship between clusters, the source aware
connected triple (SACT) similarity is introduced with regard to their common
neighbors and the source reliability. Based on NCAI and multi-granularity
information collected among base clusterings, clusters, and data instances, we
further propose two novel consensus functions, termed weighted evidence
accumulation clustering (WEAC) and graph partitioning with multi-granularity
link analysis (GP-MGLA) respectively. The experiments are conducted on eight
real-world datasets. The experimental results demonstrate the effectiveness and
robustness of the proposed methods.Comment: The MATLAB source code of this work is available at:
https://www.researchgate.net/publication/28197031
Unsupervised ensemble of experts (EoE) framework for automatic binarization of document images
In recent years, a large number of binarization methods have been developed,
with varying performance generalization and strength against different
benchmarks. In this work, to leverage on these methods, an ensemble of experts
(EoE) framework is introduced, to efficiently combine the outputs of various
methods. The proposed framework offers a new selection process of the
binarization methods, which are actually the experts in the ensemble, by
introducing three concepts: confidentness, endorsement and schools of experts.
The framework, which is highly objective, is built based on two general
principles: (i) consolidation of saturated opinions and (ii) identification of
schools of experts. After building the endorsement graph of the ensemble for an
input document image based on the confidentness of the experts, the saturated
opinions are consolidated, and then the schools of experts are identified by
thresholding the consolidated endorsement graph. A variation of the framework,
in which no selection is made, is also introduced that combines the outputs of
all experts using endorsement-dependent weights. The EoE framework is evaluated
on the set of participating methods in the H-DIBCO'12 contest and also on an
ensemble generated from various instances of grid-based Sauvola method with
promising performance.Comment: 6-page version, Accepted to be presented in ICDAR'1
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