9 citations · 16 across the 12 of their papers we have counts for
12 papers
Hyperspectral Anomaly Detection via Group Sparse Low-Rank Tensor Factorization With Automatic Anomaly Grouping
Quan Yu, Yu-Hong Dai, Xiongjun Zhang
Low-rank tensor modeling has become an effective tool for hyperspectral anomaly detection. However, existing methods still suffer from high computational cost and limited flexibili…
A Multi-View Coupled Tensor Decomposition for Lightweight Online Adaptive Traffic Prediction
Quan Yu, Jie Ni, Yu-Hong Dai +1
Accurate online traffic prediction is essential for intelligent transportation systems, where forecasting must be performed continuously under imperfect sensing conditions. Missing…
Sparse Tucker Decomposition and Graph Regularization for High-Dimensional Time Series Forecasting
Sijia Xia, Michael K. Ng, Xiongjun Zhang
Existing methods of vector autoregressive model for multivariate time series analysis make use of low-rank matrix approximation or Tucker decomposition to reduce the dimension of t…
Low-Rank Tensor Learning by Generalized Nonconvex Regularization
Sijia Xia, Michael K. Ng, Xiongjun Zhang
In this paper, we study the problem of low-rank tensor learning, where only a few of training samples are observed and the underlying tensor has a low-rank structure. The existing…
Tensor Factorization via Transformed Tensor-Tensor Product for Image Alignment
Sijia Xia, Duo Qiu, Xiongjun Zhang
In this paper, we study the problem of a batch of linearly correlated image alignment, where the observed images are deformed by some unknown domain transformations, and corrupted…
Noisy Nonnegative Tucker Decomposition with Sparse Factors and Missing Data
Xiongjun Zhang, Michael K. Ng
Tensor decomposition is a powerful tool for extracting physically meaningful latent factors from multi-dimensional nonnegative data, and has been an increasing interest in a variet…
Nonlinear Transform Induced Tensor Nuclear Norm for Tensor Completion
Ben-Zheng Li, Xi-Le Zhao, Teng-Yu Ji +2
The linear transform-based tensor nuclear norm (TNN) methods have recently obtained promising results for tensor completion. The main idea of this type of methods is exploiting the…
Tensor Completion by Multi-Rank via Unitary Transformation
Guang-Jing Song, Michael K. Ng, Xiongjun Zhang
One of the key problems in tensor completion is the number of uniformly random sample entries required for recovery guarantee. The main aim of this paper is to study $n_1 \times n_…
Tensor Completion via Convolutional Sparse Coding Regularization
Zhebin Wu, Tianchi Liao, Chuan Chen +3
Tensor data often suffer from missing value problem due to the complex high-dimensional structure while acquiring them. To complete the missing information, lots of Low-Rank Tensor…
Sparse Nonnegative Tensor Factorization and Completion with Noisy Observations
Xiongjun Zhang, Michael K. Ng
In this paper, we study the sparse nonnegative tensor factorization and completion problem from partial and noisy observations for third-order tensors. Because of sparsity and nonn…
Orthogonal Nonnegative Tucker Decomposition
Junjun Pan, Michael K. Ng, Ye Liu +2
In this paper, we study the nonnegative tensor data and propose an orthogonal nonnegative Tucker decomposition (ONTD). We discuss some properties of ONTD and develop a convex relax…
Robust Tensor Completion Using Transformed Tensor SVD
Guangjing Song, Michael K. Ng, Xiongjun Zhang
In this paper, we study robust tensor completion by using transformed tensor singular value decomposition (SVD), which employs unitary transform matrices instead of discrete Fourie…