20 citations · 54 across the 11 of their papers we have counts for
14 papers
Deep graph convolution neural network with non-negative matrix factorization for community discovery
Shuliang Xu, Shenglan Liu, Lin Feng
Community discovery is an important task for graph mining. Owing to the nonstructure, the high dimensionality, and the sparsity of graph data, it is not easy to obtain an appropria…
Self-Supervised Deep Graph Embedding with High-Order Information Fusion for Community Discovery
Shuliang Xu, Shenglan Liu, Lin Feng
Deep graph embedding is an important approach for community discovery. Deep graph neural network with self-supervised mechanism can obtain the low-dimensional embedding vectors of…
Angular Embedding: A New Angular Robust Principal Component Analysis
Shenglan Liu, Yang Yu
As a widely used method in machine learning, principal component analysis (PCA) shows excellent properties for dimensionality reduction. It is a serious problem that PCA is sensiti…
Local Neighbor Propagation Embedding
Shenglan Liu, Yang Yu
Manifold Learning occupies a vital role in the field of nonlinear dimensionality reduction and its ideas also serve for other relevant methods. Graph-based methods such as Graph Co…
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,…
A fast online cascaded regression algorithm for face alignment
Lin Feng, Caifeng Liu, Shenglan Liu +1
Traditional face alignment based on machine learning usually tracks the localizations of facial landmarks employing a static model trained offline where all of the training data is…