8 citations · 8 across the 13 of their papers we have counts for
18 papers
An Alternating Minimization Algorithm for Federated Sparse Orthogonal Nonnegative Matrix Factorization
Yanjiao Zhu, Xianchao Xiu, Yongyang Xiong +1
In this paper, we develop an alternating minimization algorithm for federated sparse orthogonal nonnegative matrix factorization (ONMF) with emphases on controlling client drift. T…
Learning to Select Features with Manifold Deep Unfolding PCA
Jingjing Liu, Pei Tai, Xianchao Xiu +1
Unsupervised feature selection (UFS) is of great significance in image processing and pattern recognition. Nevertheless, existing model-based methods often rely on manually tuned h…
PICANet: Physics-Informed Cascaded Asymmetric Network for Infrared Small Target Detection
Jingjing Liu, Yinchao Han, Xianchao Xiu +2
Infrared small target detection (ISTD) is an important research direction in image processing. However, existing methods are limited by severe background noise propagation and targ…
OptGraph: Large Language Models Enhanced Evolutionary Optimization Via Graph Retrieval-Augmented Generation
Xianchao Xiu, Jianhao Li, Huangyue Chen +1
Large language models (LLMs) have emerged as a powerful tool for automated evolutionary optimization, but existing methods remain limited in pattern reuse, error-aware refinement,…
Vehicle Routing Problem Meets Large Language Models: An Overview and Perspectives
Xianchao Xiu, Chong Shen, Yanjiao Zhu +1
The vehicle routing problem (VRP) is a central optimization problem in artificial intelligence, logistics automation, transportation scheduling, and industrial decision-making. VRP…
Large Language Models for Operations Research: A Comprehensive Survey
Xianchao Xiu, Jianhao Li, Jun Fan +1
Operations Research (OR) serves as a core decision-support methodology for complex systems, with significant applications across mathematics, management science, and computer scien…
Compressed Proximal Federated Learning for Non-Convex Composite Optimization on Heterogeneous Data
Pu Qiu, Chen Ouyang, Yongyang Xiong +3
Federated Composite Optimization (FCO) has emerged as a promising framework for training models with structural constraints (e.g., sparsity) in distributed edge networks. However,…
Efficient Personalized Federated PCA with Manifold Optimization for IoT Anomaly Detection
Xianchao Xiu, Chenyi Huang, Wei Zhang +1
Internet of things (IoT) networks face increasing security threats due to their distributed nature and resource constraints. Although federated learning (FL) has gained prominence…
Data-Adaptive Transformed Bilateral Tensor Low-Rank Representation for Clustering
Hui Chen, Xinjie Wang, Xianchao Xiu +1
Tensor low-rank representation (TLRR) has demonstrated significant success in image clustering. However, most existing methods rely on fixed transformations and suffer from poor ro…
Lightweight Deep Unfolding Networks with Enhanced Robustness for Infrared Small Target Detection
Jingjing Liu, Yinchao Han, Xianchao Xiu +2
Infrared small target detection (ISTD) is one of the key techniques in image processing. Although deep unfolding networks (DUNs) have demonstrated promising performance in ISTD due…
Transformer-Guided Content-Adaptive Graph Learning for Hyperspectral Unmixing
Hui Chen, Liangyu Liu, Xianchao Xiu +1
Hyperspectral unmixing (HU) targets to decompose each mixed pixel in remote sensing images into a set of endmembers and their corresponding abundances. Despite significant progress…
STAR-Net: An Interpretable Model-Aided Network for Remote Sensing Image Denoising
Jingjing Liu, Jiashun Jin, Xianchao Xiu +2
Remote sensing image (RSI) denoising is an important topic in the field of remote sensing. Despite the impressive denoising performance of RSI denoising methods, most current deep…
Robust Sparse Phase Retrieval: Statistical Guarantee, Optimality Theory and Convergent Algorithm
Jun Fan, Ailing Yan, Xianchao Xiu +1
Phase retrieval (PR) is a popular research topic in signal processing and machine learning. However, its performance degrades significantly when the measurements are corrupted by n…
Bi-Level Unsupervised Feature Selection
Jingjing Liu, Xiansen Ju, Xianchao Xiu +1
Unsupervised feature selection (UFS) is an important task in data engineering. However, most UFS methods construct models from a single perspective and often fail to simultaneously…
Sparse Tensor CCA via Manifold Optimization for Multi-View Learning
Yanjiao Zhu, Wanquan Liu, Xianchao Xiu +1
Tensor canonical correlation analysis (TCCA) has garnered significant attention due to its effectiveness in capturing high-order correlations in multi-view learning. However, exist…
Adaptive Multi-Order Graph Regularized NMF with Dual Sparsity for Hyperspectral Unmixing
Hui Chen, Liangyu Liu, Xianchao Xiu +1
Hyperspectral unmixing (HU) is a critical yet challenging task in remote sensing. However, existing nonnegative matrix factorization (NMF) methods with graph learning mostly focus…
Enhancing Unsupervised Feature Selection via Double Sparsity Constrained Optimization
Xianchao Xiu, Anning Yang, Chenyi Huang +2
Unsupervised feature selection (UFS) is widely applied in machine learning and pattern recognition. However, most of the existing methods only consider a single sparsity, which mak…
Bi-Sparse Unsupervised Feature Selection
Xianchao Xiu, Chenyi Huang, Pan Shang +1
To deal with high-dimensional unlabeled datasets in many areas, principal component analysis (PCA) has become a rising technique for unsupervised feature selection (UFS). However,…