دورية أكاديمية
Adaptive weighted multi-view subspace clustering method for recognizing urban functions from multi-source social sensing data
| العنوان: | Adaptive weighted multi-view subspace clustering method for recognizing urban functions from multi-source social sensing data |
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| المؤلفون: | Qiliang Liu, Zexin Lu, Weihua Huan, Chong Fan |
| المصدر: | Geo-spatial Information Science, Pp 1-25 (2024) |
| بيانات النشر: | Taylor & Francis Group, 2024. |
| سنة النشر: | 2024 |
| المجموعة: | LCC:Mathematical geography. Cartography LCC:Geodesy |
| مصطلحات موضوعية: | Urban function, social sensing data, multi-view subspace clustering, latent representation, data fusion, attention mechanism, Mathematical geography. Cartography, GA1-1776, Geodesy, QB275-343 |
| الوصف: | Multi-source social sensing data provide new opportunities to identify urban functions from the perspective of human activity. The information embedded in multi-source data typically needs to be fused to obtain a comprehensive view of urban functions. Although multi-view clustering has been successfully used to fuse multi-source social sensing data, the adaptive determination of fusion weights for high-dimensional and noisy multi-source social sensing data remains challenging. Therefore, this study proposes an adaptive weighted multi-view subspace clustering (AWMSC) method. First, we use two neural networks to map multi-source data into a common latent representation and multiple specific latent representations, which serve as the query vector and input vectors of the attention mechanism, respectively. Then, the weight of each type of data is calculated based on the attention mechanism. Finally, the specific latent representations of the multi-source data are weighted and fused into a shared subspace representation, which is used as the input of the spectral clustering algorithm to obtain clustering results. AWMSC is applied to identify urban functional zones in Beijing using bus transactions, taxi trajectories, and points of interest datasets. The results show that AWMSC outperforms the typical single-view, weighted-average, and representative multi-view methods. AWMSC can obtain a comprehensive understanding of urban functional zones which may help government departments make more accurate strategic decisions. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 10095020 1993-5153 1009-5020 |
| Relation: | https://doaj.org/toc/1009-5020; https://doaj.org/toc/1993-5153 |
| DOI: | 10.1080/10095020.2024.2356243 |
| URL الوصول: | https://doaj.org/article/24b047d5a68847cb8efdf6729495ed8f |
| رقم الأكسشن: | edsdoj.24b047d5a68847cb8efdf6729495ed8f |
| قاعدة البيانات: | Directory of Open Access Journals |
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Full Text Finder | تدمد: | 10095020 19935153 |
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| DOI: | 10.1080/10095020.2024.2356243 |