AUTHOR=Bac Jonathan , Zinovyev Andrei TITLE=Lizard Brain: Tackling Locally Low-Dimensional Yet Globally Complex Organization of Multi-Dimensional Datasets JOURNAL=Frontiers in Neurorobotics VOLUME=13 YEAR=2020 URL=https://www.frontiersin.org/journals/neurorobotics/articles/10.3389/fnbot.2019.00110 DOI=10.3389/fnbot.2019.00110 ISSN=1662-5218 ABSTRACT=

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.