Hypergraph Regularized Orthogonal Non-negative Tensor Factorization for Semi-supervised Clustering

Xianxiu Zhang

doi:10.1016/j.iot.2026.101986

Hypergraph Regularized Orthogonal Non-negative Tensor Factorization for Semi-supervised Clustering

2026-06-01

0

PRE

AI

DOI：10.1016/j.iot.2026.101986

Original

Original request for help

Save

Abstract

En 中文

Numerous high dimensional data are collected from the internet of things (IoT) because of the rapid advancement of the sensors and data science. Due to cost issues, only a portion of the data can be labeled. Thus, it is significant to construct a semi-supervised dimensionality reduction method to learn from the IoT data. Non-negative tensor factorization (NTF) has been developed as an advanced approach for clustering, achieving superior performance by maintaining the spatial structure of high-dimensional data. However, most existing NTF methods are implemented in unsupervised tasks, resulting in neglect of supervisory information. In the paper, a hypergraph regularized orthogonal non-negative tensor factorization (HONTF) framework for semi-supervised clustering is proposed. Considering the Boolean structure of the ideal representation matrix which can be divided into labeled and unlabeled parts, the proposed method integrates the semi-supervised learning into the orthogonal NTF framework. Furthermore, hypergraph regularization is incorporated to capture multivariate relationships in low-dimensional space. Based on the HONTF framework, CP (CANDECOMP/PARAFAC) decomposition and Tucker decomposition are adopted to be the basic tensor factorization methods, respectively, exhibiting the scalability of the proposed framework. Extensive comparative experiments with six state-of-the-art semi-supervised methods on eight common real-world datasets and one IOT dataset demonstrate the superiority and effectiveness of the proposed methods.

Hypergraph Regularized Orthogonal Non-negative Tensor Factorization for Semi-supervised Clustering

Abstract

Journal

Organization