Class-specific pre-trained sparse autoencoders for learning effective features for document classification

Sparse autoencoder is a commonly used deep learning approach for automatically learning features from unlabelled data (unsupervised feature learning). This paper proposes class-specific (supervised) pre-trained approach based on sparse autoencoder to gain low-dimensional interesting structure of fea...

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Bibliographic Details
Published in:2016 8th Computer Science and Electronic Engineering (CEEC) pp. 36 - 41
Main Authors: Abdulhussain, Maysa I., Gan, John Q.
Format: Conference Proceeding
Language:English
Published: IEEE 01.09.2016
Subjects:
Big data
Computer science
deep learning
document categorization
Electronic mail
Feature extraction
feature learning
Machine learning
Sparse autoencoder
Support vector machines
Training
Online Access:Get full text
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Summary:Sparse autoencoder is a commonly used deep learning approach for automatically learning features from unlabelled data (unsupervised feature learning). This paper proposes class-specific (supervised) pre-trained approach based on sparse autoencoder to gain low-dimensional interesting structure of features with high performance in document classification. Experimental results have demonstrated the advantages and usefulness of the proposed method in document classification in high-dimensional feature space, in terms of the limited number of features required to achieve good classification accuracy.
DOI:10.1109/CEEC.2016.7835885