activity
20192021
most citedMulti-view Locality Low-rank Embedding for Dimension Reduction

1 citations · 2 across the 5 of their papers we have counts for

collaborators

6 papers

cs.CV2021

Locality Relationship Constrained Multi-view Clustering Framework

Xiangzhu Meng, Wei Wei, Wenzhe Liu

In most practical applications, it's common to utilize multiple features from different views to represent one object. Among these works, multi-view subspace-based clustering has g…

cs.LG2021

A unified framework based on graph consensus term for multi-view learning

Xiangzhu Meng, Lin Feng, Chonghui Guo

In recent years, multi-view learning technologies for various applications have attracted a surge of interest. Due to more compatible and complementary information from multiple vi…

cs.LG2021

Multimodal-Aware Weakly Supervised Metric Learning with Self-weighting Triplet Loss

Huiyuan Deng, Xiangzhu Meng, Lin Feng

In recent years, we have witnessed a surge of interests in learning a suitable distance metric from weakly supervised data. Most existing methods aim to pull all the similar sample…

cs.LG2020

Multi-view Low-rank Preserving Embedding: A Novel Method for Multi-view Representation

Xiangzhu Meng, Lin Feng, Huibing Wang

In recent years, we have witnessed a surge of interest in multi-view representation learning, which is concerned with the problem of learning representations of multi-view data. Wh…

cs.LG20191 cited

The Similarity-Consensus Regularized Multi-view Learning for Dimension Reduction

Xiangzhu Meng, Huibing Wang, Lin Feng

During the last decades, learning a low-dimensional space with discriminative information for dimension reduction (DR) has gained a surge of interest. However, it's not accessible…

cs.LG20191 cited

Multi-view Locality Low-rank Embedding for Dimension Reduction

Lin Feng, Xiangzhu Meng, Huibing Wang

During the last decades, we have witnessed a surge of interests of learning a low-dimensional space with discriminative information from one single view. Even though most of them c…