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8 December 2022 A feature extraction algorithm for missing trajectory data clustering
Xintai He, Qing Li, Runze Wang, Kun Chen
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Proceedings Volume 12474, Second International Symposium on Computer Technology and Information Science (ISCTIS 2022); 124740L (2022) https://doi.org/10.1117/12.2653456
Event: Second International Symposium on Computer Technology and Information Science (ISCTIS 2022), 2022, Guilin, China
Abstract
Feature extraction is one of the critical technologies in trajectory data clustering. Extracting useful features for trajectory clustering is a difficult problem when there are missing segments in trajectories. This paper proposes a feature extraction algorithm for missing trajectories clustering. The algorithm converts trajectories into images, a multi-layer image preprocessing method is proposed to reduce the information loss during image processing. Then build an autoencoder to extract image features. The loss function is designed according to the attention mechanism to highlight the effective information in the trajectory image. The autoencoder’s ability to handle missing trajectory segments is trained by adding artificial image masking. The effect of feature extraction is verified by clustering. Compared with the unimproved autoencoding and interpolation methods, the clustering effect of the features extracted by this algorithm is improved. At missing rate 50%, this is an increase of eight and six percentage points, respectively. It is proved that the algorithm in this paper is more suitable for missing trajectory feature extraction.
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Xintai He, Qing Li, Runze Wang, and Kun Chen "A feature extraction algorithm for missing trajectory data clustering", Proc. SPIE 12474, Second International Symposium on Computer Technology and Information Science (ISCTIS 2022), 124740L (8 December 2022); https://doi.org/10.1117/12.2653456
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KEYWORDS
Feature extraction
Image segmentation
Image processing
Computer programming
Image processing algorithms and systems
Neural networks
Systems engineering
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