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Prerequisites from Matrix Analysis

In: Fundamentals of Data Analytics

Author

Listed:
  • Rudolf Mathar

    (RWTH Aachen University, Institute for Theoretical Information Technology)

  • Gholamreza Alirezaei

    (RWTH Aachen University, Chair and Institute for Communications Engineering)

  • Emilio Balda

    (RWTH Aachen University, Institute for Theoretical Information Technology)

  • Arash Behboodi

    (RWTH Aachen University, Institute for Theoretical Information Technology)

Abstract

Linear algebra and matrix algebra provide the methodology for mapping high-dimensional data onto low-dimensional spaces. The combination of matrix analysis and optimization theory is of particular interest. This chapter focuses on elaborating tools which are prerequisite for data analytics and data processing. We will not only provide a vast overview, but will also introduce relevant theorems in detail with the derivation of proofs. We think that having deep insight into the general mathematical structure of matrix functions is extremely useful for dealing with unknown future problems.

Suggested Citation

  • Rudolf Mathar & Gholamreza Alirezaei & Emilio Balda & Arash Behboodi, 2020. "Prerequisites from Matrix Analysis," Springer Books, in: Fundamentals of Data Analytics, chapter 0, pages 9-33, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-56831-3_2
    DOI: 10.1007/978-3-030-56831-3_2
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