On-Line Network Traffic Anomaly Detection Based on Tensor Sketch
Network traffic anomaly detection is critical for advanced network applications. However,
network traffic monitoring data arrive in a streaming fashion and could be infinite, which …
network traffic monitoring data arrive in a streaming fashion and could be infinite, which …
Nonrecurrent traffic congestion detection with a coupled scalable Bayesian robust tensor factorization model
Nonrecurrent traffic congestion (NRTC) usually brings unexpected delays to commuters.
Hence, it is critical to accurately detect and recognize the NRTC in a real-time manner. The …
Hence, it is critical to accurately detect and recognize the NRTC in a real-time manner. The …
Robust tensor decomposition with kernel rescaled error loss for feature extraction and dimensionality reduction
S Zhang, X Wang - Expert Systems with Applications, 2025 - Elsevier
With the increasing demand for high-order or multi-way data, various tensor decomposition
methods have been developed for feature extraction and dimensionality reduction …
methods have been developed for feature extraction and dimensionality reduction …
Robust block tensor PCA with F-norm projection framework
X Zhang, X Wang, Z Liu, J Chen - Knowledge-Based Systems, 2024 - Elsevier
Tensor principal component analysis (TPCA), also known as Tucker decomposition, ensures
that the extracted “core tensor” maximizes the variance of the sample projections …
that the extracted “core tensor” maximizes the variance of the sample projections …
Decomposition of linear tensor transformations
C Turchetti - arXiv preprint arXiv:2309.07819, 2023 - arxiv.org
One of the main issues in computing a tensor decomposition is how to choose the number of
rank-one components, since there is no finite algorithms for determining the rank of a tensor …
rank-one components, since there is no finite algorithms for determining the rank of a tensor …
Robust tensor method based on correntropy and tensor singular value decomposition for cancer genomics data
Q Qiao, YL Gao, SS Yuan, JX Liu - 2021 IEEE International …, 2021 - ieeexplore.ieee.org
The analysis of biological sequencing data can provide significant support for researchers to
unravel the mysteries of life further. This paper proposes a robust tensor data analysis …
unravel the mysteries of life further. This paper proposes a robust tensor data analysis …
Image representation and reconstruction by compositing Gaussian ellipses
CC Cheng - IET Image Processing, 2024 - Wiley Online Library
In this paper, a method of stroke‐based rendering is proposed for image representation and
reconstruction. The proposed method involves compositing a set of ellipses that greatly vary …
reconstruction. The proposed method involves compositing a set of ellipses that greatly vary …
A unified weight learning and low-rank regression model for robust complex error modeling
One of the most important problems in regression-based error model is modeling the
complex representation error caused by various corruptions and environment changes in …
complex representation error caused by various corruptions and environment changes in …
Order determination for tensor-valued observations using data augmentation
U Radojicic, N Lietzen, K Nordhausen… - arXiv preprint arXiv …, 2022 - arxiv.org
Tensor-valued data benefits greatly from dimension reduction as the reduction in size is
exponential in the number of modes. To achieve maximal reduction without loss in …
exponential in the number of modes. To achieve maximal reduction without loss in …
Challenging the Deployment of Fiducial Points in Minimum Error Entropy
In this paper, robust linear adaptive filtering in presence of non-Gaussian noise is
addressed. More precisely, the well-known algorithm for robust adaptive learning called …
addressed. More precisely, the well-known algorithm for robust adaptive learning called …