Article Instance

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{
    "pk": 30629,
    "title": "Dimensionality-Reduction and Constraint in Later Vision",
    "subtitle": null,
    "abstract": "A computational tool is presented for maintaining and accessing knowledge of certain types of constraint in data: when data samples in an n-dimensional feature space are all constrained to lie on an m-dimensional surface, m < n, they can be encoded more concisely and economically in terms of location on the m-dimensional surface than in terms of the n feature coordinates. The receding of data in this way is called dimensionality-reduction. Dimensionality-reduction may prove a useful computational tool relevant to later visual processing. Examples are presented from shape analysis.",
    "language": "eng",
    "license": {
        "name": "",
        "short_name": "",
        "text": null,
        "url": ""
    },
    "keywords": [
        {
            "word": "computational vision"
        },
        {
            "word": "dimensionality-reduction"
        },
        {
            "word": "connectionist"
        }
    ],
    "section": "Artificial Intelligence and Simulation II",
    "is_remote": true,
    "remote_url": "https://escholarship.org/uc/item/785071c9",
    "frozenauthors": [
        {
            "first_name": "Eric",
            "middle_name": "",
            "last_name": "Saund",
            "name_suffix": "",
            "institution": "Massachusetts Institute of Technology",
            "department": ""
        }
    ],
    "date_submitted": null,
    "date_accepted": null,
    "date_published": "1987-01-01T18:00:00Z",
    "render_galley": null,
    "galleys": [
        {
            "label": "PDF",
            "type": "pdf",
            "path": "https://journalpub.escholarship.org/cognitivesciencesociety/article/30629/galley/20478/download/"
        }
    ]
}