Journal
IEEE ACCESS
Volume 7, Issue -, Pages 49641-49652Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2019.2910406
Keywords
Semi-supervised learning; convolutional neural network; fuzzy rough C-mean clustering; feature learning; and image classification
Categories
Funding
- National Basic Research Program (973 Program) of China [2013CB329402]
- National Natural Science Foundation of China [61573267, 61473215, 61571342, 61572383, 61501353, 61502369, 61271302, 61272282, 61202176]
- Fund for Foreign Scholars in University Research and Teaching Programs (111 Project) [B07048]
- Major Research Plan of the National Natural Science Foundation of China [91438201, 91438103]
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While deep learning (DL) has proven to be a powerful paradigm for the classification of the large-scale big image data set. Deep learning network such as CNN requires a large number of labeled samples for training the network. However, often times the labeled data are difficult, expensive, and time-consuming. In this paper, we propose a semi-supervised approach to fused fuzzy-rough C-mean clustering with convolutional neural networks (CNNs) to knowledge learning from simultaneously intra-model and inter-model relationships forming final data representation to be classified, which can be achieved by better performance. Conception behind this is to reduce the uncertainty in terms of vagueness and indiscernibility by using fuzzy-rough C-mean clustering and specifically removing noise samples by using CNN from raw data. The framework of our proposed semi-supervised approach uses all the training data with abundant unlabeled data with few labeled data to train FRCNN model. To show the effectiveness of our model, we used four benchmark large-scale image datasets and also compared it with the state-of-the-art supervised, unsupervised, and semi-supervised learning methods for image classification.
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