Tensor Subspace

Tensor subspace learning focuses on analyzing and processing high-dimensional data represented as tensors, aiming to extract meaningful information and improve model performance by exploiting the inherent multi-linear structure. Current research emphasizes developing efficient algorithms, such as those based on singular value decomposition and low-rank tensor approximations, for applications in diverse fields including plant disease detection, semiconductor manufacturing process control, and multi-view clustering. These advancements offer improved accuracy, reduced computational complexity, and enhanced robustness in handling complex, high-dimensional datasets, leading to more effective solutions in various scientific and engineering domains.

Papers

May 30, 2024

Enhancing Plant Disease Detection: A Novel CNN-Based Approach with Tensor Subspace Learning and HOWSVD-MD
Abdelmalik Ouamane, Ammar Chouchane, Yassine Himeur, Abderrazak Debilou, Abbes Amira, Shadi Atalla, Wathiq Mansoor, Hussain Al Ahmad
Constructive Approach Long Form Novel Plant Disease Multilinear Discriminant Analysis Higher Order Singular Value Decomposition Tensor Subspace Tomato Disease

January 31, 2024

Tensor-based process control and monitoring for semiconductor manufacturing with unstable disturbances
Yanrong Li, Juan Du, Fugee Tsung, Wei Jiang
High Dimensional Expert Driven Monitoring Semiconductor Manufacturing High Dimensional Noise Tensor Subspace

November 3, 2023

Efficient Generalized Low-Rank Tensor Contextual Bandits
Qianxin Yi, Yiyang Yang, Shaojie Tang, Jiapeng Liu, Yao Wang
Contextual Bandit Bandit Algorithm Linear Contextual Bandit Low Rank Tensor Tensor Time Series Tensor Subspace

April 22, 2023

Hyper-Laplacian Regularized Concept Factorization in Low-rank Tensor Space for Multi-view Clustering
Zixiao Yu, Lele Fu, Zhiling Cai, Zhoumin Lu
Multi View Clustering Tensor Clustering Schatten $P$ Norm Concept FacTorization Tensor Subspace

April 9, 2020

TensorProjection Layer: A Tensor-Based Dimension Reduction Method in Deep Neural Networks
Toshinari Morimoto, Su-Yun Huang
Deep Neural Network Convolutional Layer Input Tensor Tensor Subspace

Tensor Subspace

Papers

Enhancing Plant Disease Detection: A Novel CNN-Based Approach with Tensor Subspace Learning and HOWSVD-MD

Tensor-based process control and monitoring for semiconductor manufacturing with unstable disturbances

Efficient Generalized Low-Rank Tensor Contextual Bandits

Hyper-Laplacian Regularized Concept Factorization in Low-rank Tensor Space for Multi-view Clustering

TensorProjection Layer: A Tensor-Based Dimension Reduction Method in Deep Neural Networks