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- W3041873191 abstract "The appropriate representation of high-dimensional data is the main focus of machine learning, pattern recognition and computer vision. With the same motivation, the F-transform uses fuzzy partitions in order to establish a compressed representation of data. Two distinguished properties of the F-transform: the best approximation in a local sense and dimensionality reduction contributed to the fact that the F-transform has many successful applications. We show that the technique of F-transform fully agrees with the technique of dimensionality reduction, based on Laplacian eigenmaps. To justify this claim, we characterize the processed by the F-transform data in terms of the adjacency graph that reflects their (data) intrinsic geometry. An application to the problem of image restoration is given." @default.
- W3041873191 created "2020-07-16" @default.
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- W3041873191 date "2020-07-11" @default.
- W3041873191 modified "2023-09-25" @default.
- W3041873191 title "Dimensionality Reduction: From Fuzzy Partitions to F-Transforms" @default.
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- W3041873191 doi "https://doi.org/10.1007/978-3-030-47124-8_32" @default.
- W3041873191 hasPublicationYear "2020" @default.
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