Dimensionality reduction methods are preprocessing techniques used for coping with high dimensionality. They have the aim of projecting the original data set of dimensionality N, without information loss, onto a lower M-dimensional submanifold. Since the value of M is unknown, techniques that allow knowing in advance the value of M, called intrinsic dimension (ID), are quite useful. The aim of the paper is to review state-of-the-art of the methods of ID estimation, underlining the recent advances and the open problems.
Intrinsic dimension estimation: Advances and open problems
CAMASTRA, Francesco;STAIANO, Antonino
2016-01-01
Abstract
Dimensionality reduction methods are preprocessing techniques used for coping with high dimensionality. They have the aim of projecting the original data set of dimensionality N, without information loss, onto a lower M-dimensional submanifold. Since the value of M is unknown, techniques that allow knowing in advance the value of M, called intrinsic dimension (ID), are quite useful. The aim of the paper is to review state-of-the-art of the methods of ID estimation, underlining the recent advances and the open problems.File in questo prodotto:
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