8 citations · 8 across the 22 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
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…