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Granular-ball computing: an efficient, robust, and interpretable adaptive multi-granularity representation and computation method
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To overcome the limitations of point-based inputs, overly fine computation and limited adaptability in existing artificial intelligence methods, Guoyin Wang and Shuyin Xia proposed granular-ball computing as a new artificial intelligence learning paradigm. Unlike traditional clustering, which mainly performs macro-level grouping, granular-ball computing uses differently sized hyperspheres, termed granular balls, as mesoscopic representation units; rectangles and ellipsoids can serve as approximate balls in low-dimensional spaces. It adaptively fits arbitrary data distributions, replacing traditional artificial intelligence computation based on fine-grained point inputs or single-granularity modeling and establishing a new theoretical paradigm for artificial intelligence based on granular balls. It aims to build an end-to-end multigranular artificial intelligence framework that improves the efficiency, robustness, and interpretability of existing methods. Recently, this theory has advanced rapidly and yielded representative results, yet it still lacks a unified model for systematic summarization. Accordingly, this article first proposes a general representation model of granular-ball computing within a unified descriptive framework and systematically reviews its fundamental ideas and advances in granular-ball computing across granular-ball supervised learning, granular-ball unsupervised learning, approximate granular-ball representation and computation, granular-ball deep learning based on latent-space granulation, granular-ball graph learning, and granular-ballinterdisciplinary research. Further, it identifies open challenges and outlines future research directions.
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Cited by 8 Pith papers
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3DGBGS: 3D Granular Ball Gaussian Splatting for Compact Novel View Synthesis
Using granular-ball point clusters to initialize anchors and Gaussian scales reduces 3D Gaussian Splatting model size by about 10% with near-identical rendering quality.
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A Boundary-Aware Non-parametric Granular-Ball Classifier Based on Minimum Description Length
MDL-GBC constructs class-conditional granular balls by comparing single-ball, two-ball, and core-boundary models under a unified MDL criterion and aggregates them for prediction, achieving the best average accuracy an...
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MDL-GBG: A Non-parametric and Interpretable Granular-Ball Generation Method for Clustering
MDL-GBG selects the shortest-description-length model among single-ball, two-ball, and core-ball-plus-residual options to generate stable granular balls that improve downstream clustering on UCI datasets.
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MDL-GBG: A Non-parametric and Interpretable Granular-Ball Generation Method for Clustering
A clustering preprocessing method that uses minimum-description-length model competition to decide when to keep, split, or peel granular balls, improving downstream clustering on UCI benchmarks.
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MDL-GBG: A Non-parametric and Interpretable Granular-Ball Generation Method for Clustering
MDL-GBG generates granular balls for clustering by selecting the shortest-description-length model among single-ball, two-ball, and core-ball-plus-residual options, then reassigns residuals, achieving competitive perf...
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Minimum Description Length based Granular-Ball Tree Regularization for Spectral Clustering
MDL-GBTRSC constructs a granular-ball tree with local MDL selection and reciprocal neighborhood continuity to regularize the affinity graph in spectral clustering.
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Minimum Description Length based Granular-Ball Tree Regularization for Spectral Clustering
MDL-GBTRSC builds an MDL-selected granular-ball tree to regularize affinity graphs in spectral clustering and reports top average ARI and NMI versus baselines.
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Efficient and Scalable Granular-ball Graph Coarsening Method for Large-scale Graph Node Classification
A multi-granularity granular-ball coarsening algorithm reduces large graphs in linear time for faster GCN training on node classification, with experiments claiming superior performance over prior methods.
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