Instance-Wise Denoising Autoencoder for High Dimensional Data
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 DA...
Main Authors: | Lin Chen, Wan-Yu Deng |
---|---|
Format: | Article |
Language: | English |
Published: |
Hindawi Limited
2016-01-01
|
Series: | Mathematical Problems in Engineering |
Online Access: | http://dx.doi.org/10.1155/2016/4365372 |
Similar Items
-
Denoising stacked autoencoders for transient electromagnetic signal denoising
by: F. Lin, et al.
Published: (2019-03-01) -
Using a denoising autoencoder for localization : Denoising cellular-based wireless localization data
by: Danielsson, Alexander, et al.
Published: (2021) -
Supervised Distributed Multi-Instance and Unsupervised Single-Instance Autoencoder Machine Learning for Damage Diagnostics with High-Dimensional Data—A Hybrid Approach and Comparison Study
by: Stefan Bosse, et al.
Published: (2021-03-01) -
Sparse Convolutional Denoising Autoencoders for Genotype Imputation
by: Junjie Chen, et al.
Published: (2019-08-01) -
Sparsity-Penalized Stacked Denoising Autoencoders for Imputing Single-Cell RNA-Seq Data
by: Weilai Chi, et al.
Published: (2020-05-01)