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The Optimally Designed Variational Autoencoder Networks for Clustering and Recovery of Incomplete Multimedia Data

School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
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Sensors 2019, 19(4), 809; https://doi.org/10.3390/s19040809
Received: 29 January 2019 / Revised: 12 February 2019 / Accepted: 13 February 2019 / Published: 16 February 2019
(This article belongs to the Special Issue Document-Image Related Visual Sensors and Machine Learning Techniques)
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Abstract

Clustering analysis of massive data in wireless multimedia sensor networks (WMSN) has become a hot topic. However, most data clustering algorithms have difficulty in obtaining latent nonlinear correlations of data features, resulting in a low clustering accuracy. In addition, it is difficult to extract features from missing or corrupted data, so incomplete data are widely used in practical work. In this paper, the optimally designed variational autoencoder networks is proposed for extracting features of incomplete data and using high-order fuzzy c-means algorithm (HOFCM) to improve cluster performance of incomplete data. Specifically, the feature extraction model is improved by using variational autoencoder to learn the feature of incomplete data. To capture nonlinear correlations in different heterogeneous data patterns, tensor based fuzzy c-means algorithm is used to cluster low-dimensional features. The tensor distance is used as the distance measure to capture the unknown correlations of data as much as possible. Finally, in the case that the clustering results are obtained, the missing data can be restored by using the low-dimensional features. Experiments on real datasets show that the proposed algorithm not only can improve the clustering performance of incomplete data effectively, but also can fill in missing features and get better data reconstruction results. View Full-Text
Keywords: feature learning; incomplete multimedia data; fuzzy c-means; variational autoencoder feature learning; incomplete multimedia data; fuzzy c-means; variational autoencoder
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Yu, X.; Li, H.; Zhang, Z.; Gan, C. The Optimally Designed Variational Autoencoder Networks for Clustering and Recovery of Incomplete Multimedia Data. Sensors 2019, 19, 809.

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