[1]YANG Wenyuan.Unsupervised dimensionality reduction of multi-label learning via autoencoder networks[J].CAAI Transactions on Intelligent Systems,2018,13(5):808-817.[doi:10.11992/tis.201804051]
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Unsupervised dimensionality reduction of multi-label learning via autoencoder networks

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 13 Number of periods: 2018 5 Page number: 808-817 Column: 学术论文—人工智能基础 Public date: 2018-09-05
Title:
Unsupervised dimensionality reduction of multi-label learning via autoencoder networks
Author(s):
YANG Wenyuan
Fujian Key Laboratory of Granular Computing and Application, Minnan Normal University, Zhangzhou 363000, China
Keywords:
multi-label learning; dimensionality reduction; unsupervised learning; neural networks; autoencoder; machine learning; deep learning; feature extraction
CLC:
TP183
DOI:
10.11992/tis.201804051
Abstract:
Multi-label learning is a machine learning framework that simultaneously deals with data associated with a group of labels. It is one of the hot spots in the field of machine learning. However, dimensionality reduction is a significant and challenging task in multi-label learning. In this paper, we propose a unsupervised dimensionality reduction method for supervised multi-label dimensiona reduction methods via autoencoder networks. Firstly, we build the autoencoder neural network to encode the input data and then decode them for output. Then we introduce the sparse constraint to calculate the overall cost, and further use the gradient descent method to iterate them. Finally, we obtain the autoencoder network learning model by deep learning training, and then extract data features to reduce dimensionality. In the experiments, we use a multi-label algorithm (ML-kNN) as the classifier, and compare them with four other methods on six publicly available datasets. Experimental results show that the proposed method can effectively extract features without using label learning; thus, it reduces multi-label data dimensionality and steadily improves the performance of multi-label learning.
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