Abstract
Unsupervised dimensionality reduction presents one of the greatest challenges facing the machine learning community. However, as for 2-dimensional time series data processing, conventional unsupervised dimensionality reduction methods usually focus on the similarity calculation techniques through 1-dimensional (1D) feature vectors, while ignoring the underlying structural and time clues. In fact, the structural information implies the spatial relationship between objects in an image and the time information holds the temporal correlation among images, both are crucial discriminative information for unsupervised dimensionality reduction. In view of this,we appeal to take structural and temporal clues into account in unsupervised dimensionality reduction. Specifically, we derive a similarity learning model, which projects the original data space to a compact feature subspace by jointly exploring the adaptive structural and temporal clues (2d-UDRAT). Besides, a practical iterative strategy is conceived to obtain the solution of the proposed model. To verify the efficiency of our method, we take video stream as an example and elaborately design a series of comparison experiments versus several 2D unsupervised dimensionality reduction methods. The experimental results indicate that our proposed is more effective than other state-of-the-art methods.
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Acknowledgements
This work was supported in part by the National Science Foundation of China under Grant No.62072372, 61902318, 61973250, and the key Research and Development Program of Shaanxi Province under Grant 2019GY-012.
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Shi, M., Guo, J., An, J. et al. Unsupervised 2D dimensionality reduction by jointly learning structural and temporal correlation. Appl Intell 52, 5646–5656 (2022). https://doi.org/10.1007/s10489-021-02439-7
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DOI: https://doi.org/10.1007/s10489-021-02439-7