10 citations · 24 across the 10 of their papers we have counts for
10 papers
GMM Discriminant Analysis with Noisy Label for Each Class
Jian-wei Liu, Zheng-ping Ren, Run-kun Lu +1
Real world datasets often contain noisy labels, and learning from such datasets using standard classification approaches may not produce the desired performance. In this paper, we…
Bilevel Online Deep Learning in Non-stationary Environment
Ya-nan Han, Jian-wei Liu, Bing-biao Xiao +2
Recent years have witnessed enormous progress of online learning. However, a major challenge on the road to artificial agents is concept drift, that is, the data probability distri…
Online Deep Learning based on Auto-Encoder
Si-si Zhang, Jian-wei Liu, Xin Zuo +2
Online learning is an important technical means for sketching massive real-time and high-speed data. Although this direction has attracted intensive attention, most of the literatu…
Multi-View representation learning in Multi-Task Scene
Run-kun Lu, Jian-wei Liu, Si-ming Lian +1
Over recent decades have witnessed considerable progress in whether multi-task learning or multi-view learning, but the situation that consider both learning scenes simultaneously…
Multi-granularity Relabeled Under-sampling Algorithm for Imbalanced Data
Qi Dai, Jian-wei Liu, Yang Liu
The imbalanced classification problem turns out to be one of the important and challenging problems in data mining and machine learning. The performances of traditional classifiers…
Auto-Encoder based Co-Training Multi-View Representation Learning
Run-kun Lu, Jian-wei Liu, Yuan-fang Wang +2
Multi-view learning is a learning problem that utilizes the various representations of an object to mine valuable knowledge and improve the performance of learning algorithm, and o…
Multi-View Non-negative Matrix Factorization Discriminant Learning via Cross Entropy Loss
Jian-wei Liu, Yuan-fang Wang, Run-kun Lu +1
Multi-view learning accomplishes the task objectives of classification by leverag-ing the relationships between different views of the same object. Most existing methods usually fo…
Multi-view Subspace Adaptive Learning via Autoencoder and Attention
Jian-wei Liu, Hao-jie Xie, Run-kun Lu +1
Multi-view learning can cover all features of data samples more comprehensively, so multi-view learning has attracted widespread attention. Traditional subspace clustering methods,…
Self-attention Multi-view Representation Learning with Diversity-promoting Complementarity
Jian-wei Liu, Xi-hao Ding, Run-kun Lu +1
Multi-view learning attempts to generate a model with a better performance by exploiting the consensus and/or complementarity among multi-view data. However, in terms of complement…
Attentive Multi-View Deep Subspace Clustering Net
Run-kun Lu, Jian-wei Liu, Xin Zuo
In this paper, we propose a novel Attentive Multi-View Deep Subspace Nets (AMVDSN), which deeply explores underlying consistent and view-specific information from multiple views an…