Marginalized Stacked Denoising Autoencoder With Adaptive Noise Probability for Cross Domain Classification

Cross-domain classification is a challenging problem, in which, how to learn domain invariant features is critical. Recently, significant improvements to this problem have emerged with the wide application of deep learning models, which have been proposed to learn higher level and robust feature rep...

Full description

Saved in:
Bibliographic Details
Published in:IEEE access Vol. 7; pp. 143015 - 143024
Main Authors: Zhang, Yuhong, Yang, Shuai, Li, Peipei, Hu, Xuegang, Wang, Hao
Format: Journal Article
Language:English
Published: Piscataway IEEE 2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Adaptation models
adaptive parameter
Classification
Deep learning
Domain adaptation
Domains
Feature extraction
Indexes
Machine learning
mSDA
Neural networks
Noise
noise probability
Noise reduction
Noise sensitivity
Task analysis
ISSN:2169-3536, 2169-3536
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Cross-domain classification is a challenging problem, in which, how to learn domain invariant features is critical. Recently, significant improvements to this problem have emerged with the wide application of deep learning models, which have been proposed to learn higher level and robust feature representation. Marginalized stacked denoising autoencoder model (mSDA) has proved to be effective to address this problem. However, the performance of mSDA is sensitive to the noise probability. In previous works, the noise probability is usually set as a constant value by cross-validation in the source domain. There is few work focus on the relationship between the noise probability and cross-domain task. In this paper, we try to compute the value of noise probability adaptively. Thus, an approach called Marginalized Stacked Denoising Autoencoders with Adaptive noise Probability (mSDA-AP) is proposed. Firstly, we extract an informative feature space by an improved index, weighted log-likehood ratio (IWLLR), then aggregate these informative features by weighting. Secondly, we compute the value of noise probability adaptively according to the distance between source domain and target domain, and then with the adaptive noise probability, we disturb the input data to learn a stronger feature space with mSDA. Finally, experimental results show the effectiveness of our proposed approach.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2925811