67 citations · 243 across the 18 of their papers we have counts for
9 papers · 1 filter
Unsupervised feature selection via self-paced learning and low-redundant regularization
Weiyi Li, Hongmei Chen, Tianrui Li +2
Much more attention has been paid to unsupervised feature selection nowadays due to the emergence of massive unlabeled data. The distribution of samples and the latent effect of tr…
Adaptive Graph-based Generalized Regression Model for Unsupervised Feature Selection
Yanyong Huang, Zongxin Shen, Fuxu Cai +2
Unsupervised feature selection is an important method to reduce dimensions of high dimensional data without labels, which is benefit to avoid ``curse of dimensionality'' and improv…
Fairness and Accuracy in Federated Learning
Wei Huang, Tianrui Li, Dexian Wang +2
In the federated learning setting, multiple clients jointly train a model under the coordination of the central server, while the training data is kept on the client to ensure priv…
Urban flows prediction from spatial-temporal data using machine learning: A survey
Peng Xie, Tianrui Li, Jia Liu +3
Urban spatial-temporal flows prediction is of great importance to traffic management, land use, public safety, etc. Urban flows are affected by several complex and dynamic factors,…
Deep Air Quality Forecasting Using Hybrid Deep Learning Framework
Shengdong Du, Tianrui Li, Yan Yang +1
Air quality forecasting has been regarded as the key problem of air pollution early warning and control management. In this paper, we propose a novel deep learning model for air qu…
Unsupervised Feature Learning Architecture with Multi-clustering Integration RBM
Jielei Chu, Hongjun Wang, Jing Liu +2
In this paper, we present a novel unsupervised feature learning architecture, which consists of a multi-clustering integration module and a variant of RBM termed multi-clustering i…
A Hybrid Method for Traffic Flow Forecasting Using Multimodal Deep Learning
Shengdong Du, Tianrui Li, Xun Gong +1
Traffic flow forecasting has been regarded as a key problem of intelligent transport systems. In this work, we propose a hybrid multimodal deep learning method for short-term traff…
Reconstruction of Hidden Representation for Robust Feature Extraction
Zeng Yu, Tianrui Li, Ning Yu +3
This paper aims to develop a new and robust approach to feature representation. Motivated by the success of Auto-Encoders, we first theoretical summarize the general properties of…
Restricted Boltzmann Machines with Gaussian Visible Units Guided by Pairwise Constraints
Jielei Chu, Hongjun Wang, Hua Meng +2
Restricted Boltzmann machines (RBMs) and their variants are usually trained by contrastive divergence (CD) learning, but the training procedure is an unsupervised learning approach…