8 citations · 8 across the 22 of their papers we have counts for
29 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…
Newton Deep Unfolding for Compressed Sensing
Changhua He, Xianchao Xiu
Compressed sensing (CS) reconstructs images from highly limited measurements, but existing deep unfolding methods are typically driven by first-order optimization and weakly exploi…
Beyond Noise Steering: Dual-Latent Space Reinforcement Learning for Generative Robot Policy
Pengfei Zhang, Teng Sun, Xianchao Xiu
Pretrained generative robot policies learn expressive action priors from demonstrations. However, existing reinforcement learning methods only steer the noisy space but fail to mod…
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…
New Bounds for Exact Penalized Cardinality-Constrained Optimization with Pseudonormality Conditions
Lili Pan, Huilin Xie, Xianchao Xiu +1
Cardinality-constrained optimization (CCO) is a popular topic in sparse learning and signal recovery, yet remains challenging due to the inherent nonconvexity and discontinuity of…
Heaviside Low-Rank Support Matrix Machine
Xianchao Xiu, Shenghao Sun, Xinrong Li +1
Support matrix machine (SMM) is an emerging classification framework that directly handles matrix-structured observations, thereby avoiding the spatial correlations destroyed by ve…
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…
GoPrune: Accelerated Structured Pruning with -Norm Optimization
Li Xu, Xianchao Xiu
Convolutional neural networks (CNNs) suffer from rapidly increasing storage and computational costs as their depth grows, which severely hinders their deployment on resource-constr…
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…
Robust Orthogonal NMF with Label Propagation for Image Clustering
Jingjing Liu, Nian Wu, Xianchao Xiu +1
Non-negative matrix factorization (NMF) is a popular unsupervised learning approach widely used in image clustering. However, in real-world clustering scenarios, most existing NMF…
Tuning-Free Structured Sparse PCA via Deep Unfolding Networks
Long Chen, Xianchao Xiu
Sparse principal component analysis (PCA) is a well-established dimensionality reduction technique that is often used for unsupervised feature selection (UFS). However, determining…
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…
LLM4FS: Leveraging Large Language Models for Feature Selection
Jianhao Li, Xianchao Xiu
Recent advances in large language models (LLMs) have provided new opportunities for decision-making, particularly in the task of automated feature selection. In this paper, we firs…
Federated Structured Sparse PCA for Anomaly Detection in IoT Networks
Chenyi Huang, Xianchao Xiu
Although federated learning has gained prominence as a privacy-preserving framework tailored for distributed Internet of Things (IoT) environments, current federated principal comp…
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…