Abstract
This paper proposes a k-means model based on density weighting, which is applied to the field of image classification and fused with deep neural network to train pseudo-labels. While clustering the learning features of the residual network, the network parameters are updated to achieve. The clustering performance of pseudo-labeled datasets is improved to solve the problem of scarcity of labeled data.
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Acknowledgment
This work is funded by the National Natural Science Found ation of China under Grant No. 61772180, the Key R & D plan of Hubei Province(2020BAB012).
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Wang, Y., Wang, C., Yan, L. (2024). Image Classification Based on Improved Unsupervised Clustering Algorithm. In: Hong, W., Kanaparan, G. (eds) Computer Science and Education. Computer Science and Technology. ICCSE 2023. Communications in Computer and Information Science, vol 2023. Springer, Singapore. https://doi.org/10.1007/978-981-97-0730-0_14
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DOI: https://doi.org/10.1007/978-981-97-0730-0_14
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