10 citations · 16 across the 4 of their papers we have counts for
6 papers
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…
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…
Heterogeneous Tensor Decomposition for Clustering via Manifold Optimization
Yanfeng Sun, Junbin Gao, Xia Hong +2
Tensors or multiarray data are generalizations of matrices. Tensor clustering has become a very important research topic due to the intrinsically rich structures in real-world mult…
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…