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Article Dans Une Revue Frontiers in Neurorobotics Année : 2020

Lizard Brain: Tackling Locally Low-Dimensional Yet Globally Complex Organization of Multi-Dimensional Datasets

Résumé

Machine learning deals with datasets characterized by high dimensionality. However, in many cases, the intrinsic dimensionality of the datasets is surprisingly low. For example, the dimensionality of a robot's perception space can be large and multi-modal but its variables can have more or less complex non-linear interdependencies. Thus multidimensional data point clouds can be effectively located in the vicinity of principal varieties possessing locally small dimensionality, but having a globally complicated organization which is sometimes difficult to represent with regular mathematical objects (such as manifolds). We review modern machine learning approaches for extracting low-dimensional geometries from multi-dimensional data and their applications in various scientific fields.
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Dates et versions

hal-02972302 , version 1 (20-10-2020)

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Jonathan Bac, Andrei Zinovyev. Lizard Brain: Tackling Locally Low-Dimensional Yet Globally Complex Organization of Multi-Dimensional Datasets. Frontiers in Neurorobotics, 2020, 1, ⟨10.3389/fnbot.2019.00110⟩. ⟨hal-02972302⟩
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