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Title: Learning Convolutional Neural Networks from Ordered Features of Generic Data
Convolutional neural networks (CNN) have become very popular for computer vision, text, and sequence tasks. CNNs have the advantage of being able to learn local patterns through convolution filters. However, generic datasets do not have meaningful local data correlations, because their features are assumed to be independent of each other. In this paper, we propose an approach to reorder features of a generic dataset to create feature correlations for CNN to learn feature representation, and use learned features as inputs to help improve traditional machine learning classifiers. Our experiments on benchmark data exhibit increased performance and illustrate the benefits of using CNNs for generic datasets.
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Award ID(s):
Publication Date:
Journal Name:
The 17th IEEE International Conference on Machine Learning and Applications (ICMLA)
Page Range or eLocation-ID:
897 to 900
Sponsoring Org:
National Science Foundation
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