Dimensionality reduction as information fusion enabler in large-scale group decision-making: Classification, challenges, and future directions

Abstract

[EN]Multi-source information fusion has become the cornerstone of modern intelligent decision systems, yet the explosive volume, heterogeneity and high dimensionality of crowd-contributed data severely challenge the efficiency and fairness of large-scale group decision-making (LSGDM). To transform this “data richness” into “decision wisdom”, dimensionality-reduction technologies have evolved from optional pre-processing modules to indispensable fusion-centric enablers that compress numerous decision makers into a compact but representative subgroup structure while preserving collective knowledge. By systematically reviewing 345 publications (2014–2025) retrieved from the Web of Science, this paper proposes the first information-fusion-oriented taxonomy of LSGDM dimensionality-reduction techniques, clustering them into: (i) clustering-analysis-based fusion of large-scale preference data, (ii) complex-network-based community detection that fuses relational information, and (iii) specialized hybrid methods that fuse multi-modal or semi-supervised cues. This review critically analyzes the above techniques, discusses their roles in alleviating scale dilemmas, improving decision quality and process operability, and establishes an evaluation system covering internal and external validity. Finally, it summarizes key challenges in information fusion, including dynamic streaming preferences, ultra-large sparse networks, and privacy-preserving fusion, and prospects paradigm optimization and key technologies such as semi-supervised granular ball fusion, autoencoder-based deep representation fusion, ensemble learning, and multimodal fusion, providing references for dimensionality reduction to support efficient, reliable, and fair LSGDM in the big data era.