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Instance-Wise Denoising Autoencoder for High Dimensional Data
Joint Authors
Source
Mathematical Problems in Engineering
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-10-31
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Chen, Lin& Deng, Wan-Yu. 2016. Instance-Wise Denoising Autoencoder for High Dimensional Data. Mathematical Problems in Engineering،Vol. 2016, no. 2016, pp.1-13.
https://search.emarefa.net/detail/BIM-1112188
Modern Language Association (MLA)
Chen, Lin& Deng, Wan-Yu. Instance-Wise Denoising Autoencoder for High Dimensional Data. Mathematical Problems in Engineering No. 2016 (2016), pp.1-13.
https://search.emarefa.net/detail/BIM-1112188
American Medical Association (AMA)
Chen, Lin& Deng, Wan-Yu. Instance-Wise Denoising Autoencoder for High Dimensional Data. Mathematical Problems in Engineering. 2016. Vol. 2016, no. 2016, pp.1-13.
https://search.emarefa.net/detail/BIM-1112188
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1112188