http://dx.doi.org/10.1016/j.ins.2011.02.007">
 

Document Type

Journal Article

Department/Unit

Department of Computer Science

Title

Learning latent variable models from distributed and abstracted data

Language

English

Abstract

Discovering global knowledge from distributed data sources is challenging, where the important issues include the ever-increasing data volume at the highly distributed sources and the general concern on data privacy. Properly abstracting the distributed data with a compact representation which can retain sufficient local details for global knowledge discovery in principle can address both the scalability and the data privacy challenges. This calls for the need to develop formal methodologies to support knowledge discovery on abstracted data. In this paper, we propose to abstract distributed data as Gaussian mixture models and learn a family of generative models from the abstracted data using a modified EM algorithm. To demonstrate the effectiveness of the proposed approach, we applied it to learn (a) data cluster models and (b) data manifold models, and evaluated their performance using both synthetic and benchmark data sets with promising results in terms of both effectiveness and scalability. Also, we have demonstrated that the proposed approach is robust against heterogeneous data distributions over the distributed sources. © 2011 Elsevier Inc. All rights reserved.

Keywords

Data abstraction, Distributed data mining, Gaussian mixture model, Generative topographic mapping, Model-based methods

Publication Date

2011

Source Publication Title

Information Sciences

Volume

181

Issue

14

Start Page

2964

End Page

2988

Publisher

Elsevier

ISSN (print)

00200255

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