COLLABORATIVE LEARNING OF MIXTURE MODELS USING DIFFUSION ADAPTATION

被引：0

作者：

Towfic, Zaid J. ^{[1
]}

Chen, Jianshu ^{[1
]}

Sayed, Ali H. ^{[1
]}

机构：

[1] Univ Calif Los Angeles, Dept Elect Engn, Los Angeles, CA 90024 USA

来源：

2011 IEEE INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING (MLSP) | 2011年

基金：

美国国家科学基金会;

关键词：

online-learning; Newton's method; diffusion; Expectation-Maximization; Gaussian-mixture-model; machine learning; distributed processing;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In large ad-hoc networks, classification tasks such as spam filtering, multi-camera surveillance, and advertising have been traditionally implemented in a centralized manner by means of fusion centers. These centers receive and process the information that is collected from across the network. In this paper, we develop a decentralized adaptive strategy for information processing and apply it to the task of estimating the parameters of a Gaussian-mixture-model (GMM). The proposed technique employs adaptive diffusion algorithms that enable adaptation, learning, and cooperation at local levels. The simulation results illustrate how the proposed technique outperforms non-collaborative learning and is competitive against centralized solutions.

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页数：6

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