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Semantic explorations in factorizing Boolean data via formal concepts
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DOI：10.1016/j.ijar.2024.109247.png)
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摘要
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We use now available psychological data involving human concepts, objects covered by these concepts, and binary attributes describing the objects to explore selected semantic aspects of Boolean matrix factorization. Our basic perspective derives from the intuitive requirement that the factors computed from data should represent natural categories latently present in the data. This idea is examined for factorization algorithms that utilize formal concepts to build factors. We provide several experimental observations which imply that the inspected factorization methods deliver semantically sound factors that resemble significant human concepts of the examined domains.
Keyword:
Boolean matrix factorization
Formal concept
Human concept
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