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  "author": "Jing Li",
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  "author": "Jing Li",
            
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  "citation": [
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  "citation": [
            
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    "https://doi.org/10.48550/arXiv.2305.07213"
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    "https://doi.org/10.48550/arXiv.2305.07213"
            
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  ],
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  "doi": "10.57702/1n86wx79",
            
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  "doi_date_published": "2024-12-16",
            
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  "extra_authors": [
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  "extra_authors": [
            
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    {
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    {
            
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      "extra_author": "Quanxue Gao",
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      "extra_author": "Quanxue Gao",
            
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      "orcid": ""
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      "orcid": ""
            
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    },
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    {
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      "extra_author": "Qianqian Wang",
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      "extra_author": "Qianqian Wang",
            
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      "orcid": ""
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      "orcid": ""
            
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    },
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    {
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    {
            
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      "extra_author": "Wei Xia",
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      "extra_author": "Wei Xia",
            
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      "orcid": ""
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      "orcid": ""
            
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    },
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    },
            
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    {
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    {
            
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      "extra_author": "Xinbo Gao",
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      "extra_author": "Xinbo Gao",
            
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      "orcid": ""
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      "orcid": ""
            
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    }
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      "description": "",
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      "name": "clustering",
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      "name": "clustering",
            
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      "title": "Clustering"
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      "title": "Clustering"
            
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    {
            
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      "description": "",
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      "description": "",
            
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      "display_name": "Multi-view clustering",
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      "display_name": "Multi-view clustering",
            
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      "name": "multi-view-clustering",
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      "name": "multi-view-clustering",
            
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      "title": "Multi-view clustering"
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      "title": "Multi-view clustering"
            
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    }
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  "name": "mnist4",
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  "name": "mnist4",
            
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  "notes": "Mnist has a training set of 10000 examples, and the first 
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  "notes": "Mnist has a training set of 10000 examples, and the first 
            
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four categories(0-3) of it, i.e., the first 4000 samples form a subset 
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four categories(0-3) of it, i.e., the first 4000 samples form a subset 
            
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called Mnist4. It is divided into 4 categories and described by 3 
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called Mnist4. It is divided into 4 categories and described by 3 
            
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different features: 30 Isometric Projection (IP), 9 Linear 
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different features: 30 Isometric Projection (IP), 9 Linear 
            
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Discriminant Analysis (LDA) and 30 Neigh-bourhood Preserving Embedding 
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Discriminant Analysis (LDA) and 30 Neigh-bourhood Preserving Embedding 
            
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(NPE).",
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(NPE).",
            
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distributions based on DCAT.",
            
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      "display_name": "manifold learning",
            
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      "id": "f1d7766b-2efa-4b6c-8e40-35bead200d6d",
            
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      "name": "manifold learning",
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      "name": "manifold learning",
            
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      "state": "active",
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      "state": "active",
            
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      "vocabulary_id": null
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      "vocabulary_id": null
            
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    },
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    {
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    {
            
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      "display_name": "multi-view clustering",
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      "display_name": "multi-view clustering",
            
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      "id": "4c8aaa91-ae12-42cc-8af9-c734f2e37cc1",
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      "id": "4c8aaa91-ae12-42cc-8af9-c734f2e37cc1",
            
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      "name": "multi-view clustering",
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      "name": "multi-view clustering",
            
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    }
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    }
            
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  ],
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  ],
            
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  "title": "Mnist4",
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  "title": "Mnist4",
            
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  "type": "dataset",
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  "type": "dataset",
            
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  "version": ""
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  "version": ""
            
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}
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}