EXPERIMENTAL STUDY OF MANIFOLD LEARNING AND TANGENT PROPAGATION

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dc.contributor.authorAshimov, Temirlan
dc.date.accessioned2021-05-14T07:54:02Z
dc.date.available2021-05-14T07:54:02Z
dc.date.issued2021-05
dc.description.abstractIn the Data Science routine, we often face the curse of dimensionality, dealing with high-dimensional data which, in turn, can be very difficult. The problems of this nature can be approached by methods of Dimensionality Reduction. These methods assume that data can be interpreted in a smaller dimension. The hypothesis proposed in this work is that data is located exactly or near along with a low dimension manifold and the tool for finding this manifold is auto-encoders. In particular, we calculate the basis of the tangent space of the low-dimensional manifold at each data point and up towards using it to the regularization of the regression task. All calculations are implemented via Python 3 since this programming language in cludes a wide range of packages for dealing with Big Data.en_US
dc.identifier.citationAshimov, T. (2021). Experimental study of Manifold learning and tangent propagation (Unpublished master`s thesis). Nazarbayev University, Nur-Sultan, Kazakhstanen_US
dc.identifier.urihttp://nur.nu.edu.kz/handle/123456789/5394
dc.language.isoenen_US
dc.publisherNazarbayev University School of Sciences and Humanitiesen_US
dc.rightsAttribution-NonCommercial-ShareAlike 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/us/*
dc.subjectManifold learningen_US
dc.subjectdataen_US
dc.subjectType of access: Open Accessen_US
dc.titleEXPERIMENTAL STUDY OF MANIFOLD LEARNING AND TANGENT PROPAGATIONen_US
dc.typeMaster's thesisen_US
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