Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Privacy-preserving Distributed Clustering using Generative Models
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Visualizing Global Manifold Based on Distributed Local Data Abstractions
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Service-Oriented Distributed Data Mining
IEEE Internet Computing
Graph-Based Abstraction for Privacy Preserving Manifold Visualization
WI-IATW '06 Proceedings of the 2006 IEEE/WIC/ACM international conference on Web Intelligence and Intelligent Agent Technology
Learning latent variable models from distributed and abstracted data
Information Sciences: an International Journal
Real-Time Tactical and Strategic Sales Management for Intelligent Agents Guided by Economic Regimes
Information Systems Research
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Due to the increasing demand of massive and distributed data analysis, achieving highly accurate global data analysis results with local data privacy preserved becomes an increasingly important research issue. In this paper, we propose to adopt a model-based method (Gaussian mixture model) for local data abstraction and aggregate the local model parameters for learning global models. To support global model learning based on solely local GMM parameters instead of virtual data generated from the aggregated local model, a novel EM-like algorithm is derived. Experiments have been performed using synthetic datasets and the proposed method was demonstrated to be able to achieve the global model accuracy comparable to that of using the data regeneration approach at a much lower computational cost.