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Mathematical Problems in Engineering
Volume 2016 (2016), Article ID 4365372, 13 pages
Research Article

Instance-Wise Denoising Autoencoder for High Dimensional Data

School of Computer, Xi’an University of Posts & Telecommunications, Shaanxi 710121, China

Received 9 April 2016; Revised 31 August 2016; Accepted 15 September 2016

Academic Editor: Erik Cuevas

Copyright © 2016 Lin Chen and Wan-Yu Deng. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Denoising Autoencoder (DAE) is one of the most popular fashions that has reported significant success in recent neural network research. To be specific, DAE randomly corrupts some features of the data to zero as to utilize the cooccurrence information while avoiding overfitting. However, existing DAE approaches do not fare well on sparse and high dimensional data. In this paper, we present a Denoising Autoencoder labeled here as Instance-Wise Denoising Autoencoder (IDA), which is designed to work with high dimensional and sparse data by utilizing the instance-wise cooccurrence relation instead of the feature-wise one. IDA works ahead based on the following corruption rule: if an instance vector of nonzero feature is selected, it is forced to become a zero vector. To avoid serious information loss in the event that too many instances are discarded, an ensemble of multiple independent autoencoders built on different corrupted versions of the data is considered. Extensive experimental results on high dimensional and sparse text data show the superiority of IDA in efficiency and effectiveness. IDA is also experimented on the heterogenous transfer learning setting and cross-modal retrieval to study its generality on heterogeneous feature representation.