Search Results for author: Xiuwen Yi

Found 7 papers, 0 papers with code

Adaptive Collaborative Correlation Learning-based Semi-Supervised Multi-Label Feature Selection

no code implementations18 Jun 2024 Yanyong Huang, Li Yang, Dongjie Wang, Ke Li, Xiuwen Yi, Fengmao Lv, Tianrui Li

Then, the instance correlation and label correlation are integrated into the proposed regression model to adaptively learn both the sample similarity graph and the label similarity graph, which mutually enhance feature selection performance.

feature selection Missing Labels +1

C$^{2}$IMUFS: Complementary and Consensus Learning-based Incomplete Multi-view Unsupervised Feature Selection

no code implementations20 Aug 2022 Yanyong Huang, Zongxin Shen, Yuxin Cai, Xiuwen Yi, Dongjie Wang, Fengmao Lv, Tianrui Li

Besides, learning the complete similarity graph, as an important promising technology in existing MUFS methods, cannot achieve due to the missing views.

feature selection

Incremental Unsupervised Feature Selection for Dynamic Incomplete Multi-view Data

no code implementations5 Apr 2022 Yanyong Huang, Kejun Guo, Xiuwen Yi, Zhong Li, Tianrui Li

To address these issues, we propose an Incremental Incomplete Multi-view Unsupervised Feature Selection method (I$^2$MUFS) on incomplete multi-view streaming data.

Clustering feature selection

Predicting Citywide Crowd Flows in Irregular Regions Using Multi-View Graph Convolutional Networks

no code implementations19 Mar 2019 Junkai Sun, Junbo Zhang, Qiaofei Li, Xiuwen Yi, Yuxuan Liang, Yu Zheng

In this paper, we formulate crowd flow forecasting in irregular regions as a spatio-temporal graph (STG) prediction problem in which each node represents a region with time-varying flows.

HyperST-Net: Hypernetworks for Spatio-Temporal Forecasting

no code implementations28 Sep 2018 Zheyi Pan, Yuxuan Liang, Junbo Zhang, Xiuwen Yi, Yong Yu, Yu Zheng

In this paper, we propose a general framework (HyperST-Net) based on hypernetworks for deep ST models.

Spatio-Temporal Forecasting Time Series +1

Predicting Citywide Crowd Flows Using Deep Spatio-Temporal Residual Networks

no code implementations10 Jan 2017 Junbo Zhang, Yu Zheng, Dekang Qi, Ruiyuan Li, Xiuwen Yi, Tianrui Li

We propose a deep-learning-based approach, called ST-ResNet, to collectively forecast two types of crowd flows (i. e. inflow and outflow) in each and every region of a city.


ST-MVL: Filling Missing Values in Geo-Sensory Time Series Data

no code implementations IJCAI 2016 2016 Xiuwen Yi, Yu Zheng, Junbo Zhang, Tianrui Li

In this paper, we propose a spatio-temporal multi-view-based learning (ST-MVL) method to collectively fill missing readings in a collection of geosensory time series data, considering 1) the temporal correlation between readings at different timestamps in the same series and 2) the spatial correlation between different time series.

Collaborative Filtering Multivariate Time Series Imputation +3

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