3,746 research outputs found
Decorrelation of Neutral Vector Variables: Theory and Applications
In this paper, we propose novel strategies for neutral vector variable
decorrelation. Two fundamental invertible transformations, namely serial
nonlinear transformation and parallel nonlinear transformation, are proposed to
carry out the decorrelation. For a neutral vector variable, which is not
multivariate Gaussian distributed, the conventional principal component
analysis (PCA) cannot yield mutually independent scalar variables. With the two
proposed transformations, a highly negatively correlated neutral vector can be
transformed to a set of mutually independent scalar variables with the same
degrees of freedom. We also evaluate the decorrelation performances for the
vectors generated from a single Dirichlet distribution and a mixture of
Dirichlet distributions. The mutual independence is verified with the distance
correlation measurement. The advantages of the proposed decorrelation
strategies are intensively studied and demonstrated with synthesized data and
practical application evaluations
Term-community-based topic detection with variable resolution
Network-based procedures for topic detection in huge text collections offer
an intuitive alternative to probabilistic topic models. We present in detail a
method that is especially designed with the requirements of domain experts in
mind. Like similar methods, it employs community detection in term
co-occurrence graphs, but it is enhanced by including a resolution parameter
that can be used for changing the targeted topic granularity. We also establish
a term ranking and use semantic word-embedding for presenting term communities
in a way that facilitates their interpretation. We demonstrate the application
of our method with a widely used corpus of general news articles and show the
results of detailed social-sciences expert evaluations of detected topics at
various resolutions. A comparison with topics detected by Latent Dirichlet
Allocation is also included. Finally, we discuss factors that influence topic
interpretation.Comment: 31 pages, 6 figure
Finite Bivariate and Multivariate Beta Mixture Models Learning and Applications
Finite mixture models have been revealed to provide flexibility for data clustering. They have demonstrated high competence and potential to capture hidden structure in data. Modern technological progresses, growing volumes and varieties of generated data, revolutionized computers and other related factors are contributing to produce large scale data. This fact enhances the significance of finding reliable and adaptable models which can analyze bigger, more complex data to identify latent patterns, deliver faster and more accurate results and make decisions with minimal human interaction.
Adopting the finest and most accurate distribution that appropriately represents the mixture components is critical. The most widely adopted generative model has been the Gaussian mixture. In numerous real-world applications, however, when the nature and structure of data are non-Gaussian, this modelling fails. One of the other crucial issues when using mixtures is determination of
the model complexity or number of mixture components. Minimum message length (MML) is one of the main techniques in frequentist frameworks to tackle this challenging issue.
In this work, we have designed and implemented a finite mixture model, using the bivariate and multivariate Beta
distributions for cluster analysis and demonstrated its flexibility in describing the intrinsic characteristics of the observed data.
In addition, we have applied our estimation and model selection algorithms to synthetic and real datasets. Most importantly, we considered interesting applications such as in image segmentation, software modules defect prediction, spam detection and occupancy estimation in smart buildings
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