Zobrazeno 1 - 10
of 172
pro vyhledávání: '"Yu, Gaohang"'
The hippocampus is a crucial brain structure associated with various psychiatric disorders, and its automatic and precise segmentation is essential for studying these diseases. In recent years, deep learning-based methods have made significant progre
Externí odkaz:
http://arxiv.org/abs/2407.11292
Large tensors are frequently encountered in various fields such as computer vision, scientific simulations, sensor networks, and data mining. However, these tensors are often too large for convenient processing, transfer, or storage. Fortunately, the
Externí odkaz:
http://arxiv.org/abs/2404.16580
Tensor train decomposition is one of the most powerful approaches for processing high-dimensional data. For low-rank tensor train decomposition of large tensors, the alternating least squares (ALS) algorithm is widely used by updating each core tenso
Externí odkaz:
http://arxiv.org/abs/2309.08093
Tensor train decomposition is a powerful tool for dealing with high-dimensional, large-scale tensor data, which is not suffering from the curse of dimensionality. To accelerate the calculation of the auxiliary unfolding matrix, some randomized algori
Externí odkaz:
http://arxiv.org/abs/2308.01480
The T-product method based upon Discrete Fourier Transformation (DFT) has found wide applications in engineering, in particular, in image processing. In this paper, we propose variable T-product, and apply the Zero-Padding Discrete Fourier Transforma
Externí odkaz:
http://arxiv.org/abs/2305.07837
Low-rank approximation of tensors has been widely used in high-dimensional data analysis. It usually involves singular value decomposition (SVD) of large-scale matrices with high computational complexity. Sketching is an effective data compression an
Externí odkaz:
http://arxiv.org/abs/2301.11598
Tensor train (TT) factorization and corresponding TT rank, which can well express the low-rankness and mode correlations of higher-order tensors, have attracted much attention in recent years. However, TT factorization based methods are generally not
Externí odkaz:
http://arxiv.org/abs/2205.03380
Autor:
Lv, Laishui, Zhang, Ting, Hu, Peng, Bardou, Dalal, Niu, Shanzhou, Zheng, Zijun, Yu, Gaohang, Wu, Heng
Publikováno v:
In Expert Systems With Applications 15 March 2024 238 Part D
Autor:
Qi, Liqun, Yu, Gaohang
Based upon the T-SVD (tensor SVD) of third order tensors, introduced by Kilmer and her collaborators, we define T-singular values of third order tensors. T-singular values of third order tensors are nonnegative scalars. The number of nonzero T-singul
Externí odkaz:
http://arxiv.org/abs/2103.00976
Recovery of internet network traffic data from incomplete observed data is an important issue in internet network engineering and management. In this paper, by fully combining the temporal stability and periodicity features in internet traffic data,
Externí odkaz:
http://arxiv.org/abs/2005.09838