34 citations · 77 across the 16 of their papers we have counts for
17 papers
CaEGCN: Cross-Attention Fusion based Enhanced Graph Convolutional Network for Clustering
Guangyu Huo, Yong Zhang, Junbin Gao +3
With the powerful learning ability of deep convolutional networks, deep clustering methods can extract the most discriminative information from individual data and produce more sat…
Adversarial Privacy-preserving Filter
Jiaming Zhang, Jitao Sang, Xian Zhao +3
While widely adopted in practical applications, face recognition has been critically discussed regarding the malicious use of face images and the potential privacy problems, e.g.,…
Vectorial Dimension Reduction for Tensors Based on Bayesian Inference
Fujiao Ju, Yanfeng Sun, Junbin Gao +2
Dimensionality reduction for high-order tensors is a challenging problem. In conventional approaches, higher order tensors are `vectorized` via Tucker decomposition to obtain lower…
Localized LRR on Grassmann Manifolds: An Extrinsic View
Boyue Wang, Yongli Hu, Junbin Gao +2
Subspace data representation has recently become a common practice in many computer vision tasks. It demands generalizing classical machine learning algorithms for subspace data. L…
Locality Preserving Projections for Grassmann manifold
Boyue Wang, Yongli Hu, Junbin Gao +3
Learning on Grassmann manifold has become popular in many computer vision tasks, with the strong capability to extract discriminative information for imagesets and videos. However,…
Matrix Variate RBM Model with Gaussian Distributions
Simeng Liu, Yanfeng Sun, Yongli Hu +2
Restricted Boltzmann Machine (RBM) is a particular type of random neural network models modeling vector data based on the assumption of Bernoulli distribution. For multi-dimensiona…
Partial Least Squares Regression on Riemannian Manifolds and Its Application in Classifications
Haoran Chen, Yanfeng Sun, Junbin Gao +2
Partial least squares regression (PLSR) has been a popular technique to explore the linear relationship between two datasets. However, most of algorithm implementations of PLSR may…
Laplacian LRR on Product Grassmann Manifolds for Human Activity Clustering in Multi-Camera Video Surveillance
Boyue Wang, Yongli Hu, Junbin Gao +2
In multi-camera video surveillance, it is challenging to represent videos from different cameras properly and fuse them efficiently for specific applications such as human activity…
Kernelized LRR on Grassmann Manifolds for Subspace Clustering
Boyue Wang, Yongli Hu, Junbin Gao +2
Low rank representation (LRR) has recently attracted great interest due to its pleasing efficacy in exploring low-dimensional sub- space structures embedded in data. One of its suc…
Mixture of Bilateral-Projection Two-dimensional Probabilistic Principal Component Analysis
Fujiao Ju, Yanfeng Sun, Junbin Gao +2
The probabilistic principal component analysis (PPCA) is built upon a global linear mapping, with which it is insufficient to model complex data variation. This paper proposes a mi…
Matrix Variate RBM and Its Applications
Guanglei Qi, Yanfeng Sun, Junbin Gao +2
Restricted Boltzmann Machine (RBM) is an importan- t generative model modeling vectorial data. While applying an RBM in practice to images, the data have to be vec- torized. This r…
Partial Sum Minimization of Singular Values Representation on Grassmann Manifolds
Boyue Wang, Yongli Hu, Junbin Gao +2
As a significant subspace clustering method, low rank representation (LRR) has attracted great attention in recent years. To further improve the performance of LRR and extend its a…
Block-Diagonal Sparse Representation by Learning a Linear Combination Dictionary for Recognition
Xinglin Piao, Yongli Hu, Yanfeng Sun +2
In a sparse representation based recognition scheme, it is critical to learn a desired dictionary, aiming both good representational power and discriminative performance. In this p…
Tensor Sparse and Low-Rank based Submodule Clustering Method for Multi-way Data
Xinglin Piao, Yongli Hu, Junbin Gao +3
A new submodule clustering method via sparse and low-rank representation for multi-way data is proposed in this paper. Instead of reshaping multi-way data into vectors, this method…
Fast Optimization Algorithm on Riemannian Manifolds and Its Application in Low-Rank Representation
Haoran Chen, Yanfeng Sun, Junbin Gao +1
The paper addresses the problem of optimizing a class of composite functions on Riemannian manifolds and a new first order optimization algorithm (FOA) with a fast convergence rate…
Low Rank Representation on Grassmann Manifolds: An Extrinsic Perspective
Boyue Wang, Yongli Hu, Junbin Gao +2
Many computer vision algorithms employ subspace models to represent data. The Low-rank representation (LRR) has been successfully applied in subspace clustering for which data are…
Kernelized Low Rank Representation on Grassmann Manifolds
Boyue Wang, Yongli Hu, Junbin Gao +2
Low rank representation (LRR) has recently attracted great interest due to its pleasing efficacy in exploring low-dimensional subspace structures embedded in data. One of its succe…