EXPERIMENTAL STUDY OF MANIFOLD LEARNING AND TANGENT PROPAGATION
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Date
2021-05
Authors
Ashimov, Temirlan
Journal Title
Journal ISSN
Volume Title
Publisher
Nazarbayev University School of Sciences and Humanities
Abstract
In 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.
Description
Keywords
Manifold learning, data, Type of access: Open Access
Citation
Ashimov, T. (2021). Experimental study of Manifold learning and tangent propagation (Unpublished master`s thesis). Nazarbayev University, Nur-Sultan, Kazakhstan