18 citations · 64 across the 18 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
Hierarchic Neighbors Embedding
Shenglan Liu, Yang Yu, Yang Liu +3
Manifold learning now plays a very important role in machine learning and many relevant applications. Although its superior performance in dealing with nonlinear data distribution,…
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…
Rough extreme learning machine: a new classification method based on uncertainty measure
Lin Feng, Shuliang Xu, Feilong Wang +1
Extreme learning machine (ELM) is a new single hidden layer feedback neural network. The weights of the input layer and the biases of neurons in hidden layer are randomly generated…