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Title: Random mixed field model for mixed-attribute data restoration
Authors: Li, Q
Bian, W
You, J
Tao, D
Issue Date: 2016
Publisher: AAAI press
Source: 30th AAAI Conference on Artificial Intelligence, AAAI 2016, 2016, p. 1244-1250 How to cite?
Abstract: Noisy and incomplete data restoration is a critical preprocessing step in developing effective learning algorithms, which targets to reduce the effect of noise and missing values in data. By utilizing attribute correlations and/or instance similarities, various techniques have been developed for data denoising and imputation tasks. However, current existing data restoration methods are either specifically designed for a particular task, or incapable of dealing with mixed-attribute data. In this paper, we develop a new probabilistic model to provide a general and principled method for restoring mixed-attribute data. The main contributions of this study are twofold: A) a unified generative model, utilizing a generic random mixed field (RMF) prior, is designed to exploit mixedattribute correlations; and b) a structured mean-field variational approach is proposed to solve the challenging inference problem of simultaneous denoising and imputation. We evaluate our method by classification experiments on both synthetic data and real benchmark datasets. Experiments demonstrate, our approach can effectively improve the classification accuracy of noisy and incomplete data by comparing with other data restoration methods.
Description: 30th AAAI Conference on Artificial Intelligence, AAAI 2016, Phoenix, US, 12-17 February 2016
ISBN: 9781577357605
Appears in Collections:Conference Paper

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