Journal
WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY
Volume 9, Issue 1, Pages -Publisher
WILEY PERIODICALS, INC
DOI: 10.1002/widm.1255
Keywords
deep learning; feature learning; Image classification; object recognition; width and depth of deep architectures
Funding
- Fundamental Research Funds for the Central Universities of China
- CERNET Innovation Project [NGII20170416]
- Science and Technology Program of Qingdao [17-3-3-20-nsh]
- National Natural Science Foundation of China (NSFC) [61403353]
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Since Pearson developed principal component analysis (PCA) in 1901, feature learning (or called representation learning) has been studied for more than 100 years. During this period, many shallow feature learning methods have been proposed based on various learning criteria and techniques, until the popular deep learning research in recent years. In this advanced review, we describe the historical profile of the shallow feature learning research and introduce the important developments of the deep learning models. Particularly, we survey the deep architectures with benefits from the optimization of their width and depth, as these models have achieved new records in many applications, such as image classification and object detection. Finally, several interesting directions of deep learning are presented and briefly discussed. This article is categorized under: Technologies > Classification Technologies > Machine Learning Technologies > Computational Intelligence
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