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EVNet: An Explainable Deep Network for Dimension Reduction

Autor: Zang, Zelin, Cheng, Shenghui, Lu, Linyan, Xia, Hanchen, Li, Liangyu, Sun, Yaoting, Xu, Yongjie, Shang, Lei, Sun, Baigui, Li, Stan Z.

Dimension reduction (DR) is commonly utilized to capture the intrinsic structure and transform high-dimensional data into low-dimensional space while retaining meaningful properties of the original data. It is used in various applications, such as im

Externí odkaz: http://arxiv.org/abs/2211.15478

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UDRN: Unified Dimensional Reduction Neural Network for Feature Selection and Feature Projection

Autor: Zang, Zelin, Xu, Yongjie, Lu, Linyan, Geng, Yulan, Yang, Senqiao, Li, Stan Z.

Dimensional reduction~(DR) maps high-dimensional data into a lower dimensions latent space with minimized defined optimization objectives. The DR method usually falls into feature selection~(FS) and feature projection~(FP). FS focuses on selecting a

Externí odkaz: http://arxiv.org/abs/2207.03809

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Machine Learning for Predictive Deployment of UAVs with Multiple Access

Autor: Lu, Linyan, Yang, Zhaohui, Chen, Mingzhe, Zang, Zelin, Shikh-Bahaei, Mohammad

In this paper, a machine learning based deployment framework of unmanned aerial vehicles (UAVs) is studied. In the considered model, UAVs are deployed as flying base stations (BS) to offload heavy traffic from ground BSs. Due to time-varying traffic

Externí odkaz: http://arxiv.org/abs/2003.02631

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