1 code implementation • 2 Jan 2024 • Dehua Peng, Zhipeng Gui, Wenzhang Wei, Huayi Wu
As a pivotal approach in machine learning and data science, manifold learning aims to uncover the intrinsic low-dimensional structure within complex nonlinear manifolds in high-dimensional space.
no code implementations • 31 Dec 2023 • Dehua Peng, Zhipeng Gui, Huayi Wu
By interpreting the causes of the curse of dimensionality, we can better understand the limitations of current models and algorithms, and drive to improve the performance of data analysis and machine learning tasks in high-dimensional space.
1 code implementation • 7 Dec 2023 • Dehua Peng, Zhipeng Gui, Huayi Wu
As the most typical graph clustering method, spectral clustering is popular and attractive due to the remarkable performance, easy implementation, and strong adaptability.
no code implementations • 7 Dec 2023 • Dehua Peng, Zhipeng Gui, Huayi Wu
Boundary points pose a significant challenge for machine learning tasks, including classification, clustering, and dimensionality reduction.
no code implementations • 28 Nov 2023 • Jiaqi Ruan, Xiangrui Meng, Yifan Zhu, Gaoqi Liang, Xianzhuo Sun, Huayi Wu, Huijuan Xiao, Mengqian Lu, Pin Gao, Jiapeng Li, Wai-kin Wong, Zhao Xu, Junhua Zhao
Modern society's reliance on power systems is at risk from the escalating effects of wind-related climate change.
no code implementations • 15 Sep 2023 • Wenzhang Wei, Zhipeng Gui, Changguang Wu, Anqi Zhao, Dehua Peng, Huayi Wu
In this work, we propose a Dynamic Visual Semantic Sub-Embeddings framework (DVSE) to reduce the information entropy.
1 code implementation • 6 Apr 2020 • Xinglei Wang, Xuefeng Guan, Jun Cao, Na Zhang, Huayi Wu
This model builds on sequence to sequence (seq2seq) architecture to capture temporal feature and relies on graph convolution for aggregating spatial information.
no code implementations • 14 Apr 2019 • Na Zhang, Xuefeng Guan, Jun Cao, Xinglei Wang, Huayi Wu
In this paper, we propose a hybrid approach that learns the spatio-temporal dependency in traffic flows and predicts short-term traffic speeds on a road network.