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This open access book provides a robust exposition of the mathematical foundations of data representation, focusing on two essential pillars of dimensionality reduction methods, namely geometry in general and Riemannian geometry in particular, and category theory.Presenting a list of examples consisting of both geometric objects and empirical datasets, this book provides insights into the
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Series series Princeton Puzzlers
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158th European Study Group with Industry, Barcelona, Spain, January 27–31, 2020

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Series series Springer Nature Proceedings excluding Computer Science
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Series Libro 123 - Pure and Applied Mathematics: A Wiley Series of Texts, Monographs and Tracts
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Series series Computer Science and Engineering (German Language)
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eBooks de Matemáticas aplicadas

Data Visualization with Category Theory and Geometry

Los números no mienten

Complexity

Across the Board

Applications of Industrial Mathematics

The Theory of Probability

Integrated Computer Technologies in Mechanical Engineering

The Finite Element Method: Theory, Implementation, and Applications

Multifractal Financial Markets

Time-Dependent Problems and Difference Methods

Dynamics of Mechanical Systems with Non-Ideal Excitation

Dynamical Systems in Theoretical Perspective

The Future of Work: How Technology is Transforming Jobs and Skills

Group Theoretical Methods in Physics

Nonparametric Tests for Censored Data

Quantile Regression for Cross-Sectional and Time Series Data

Modern Computational Finance

Kernel Mode Decomposition and the Programming of Kernels

X Games In Mathematics: Sports Training That Counts!

Geometry by Its Transformations

Planetary Spacecraft Navigation

Mathematical and Numerical Methods for Partial Differential Equations

Covering Walks in Graphs

Programming4Modeling