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Machine Learning and Covariance Matrices

In: Covariance Analysis and Beyond

Author

Listed:
  • Wei Lan

    (Southwestern University of Finance and Economics, School of Statistics and Data Science and Center of Statistical Research)

  • Chih-Ling Tsai

    (University of California - Davis, Graduate School of Management)

Abstract

This chapter first reviews the three types of machine learningMachine learning: supervised learningSupervised learning, unsupervised learningUnsupervised learning, and semi-supervised learningSupervised learningSemi-supervised learning, and briefly discusses reinforcement learningReinforcement learning. Subsequently, we introduce three types of deep learningDeep learning methods: Convolution Neural Networks (CNNs)Convolutional neural networks (CNNs), Graph Convolutional Networks (GCNs),Graph convolutional networks (GCNs) and Transformers, and we discuss their relationships with covariance matrices. We then introduce the concept of transfer learningTransfer learning and its usefulness in high-dimensional covariance analysis. An empirical example is presented to illustrate the application of deep learningDeep learning.

Suggested Citation

  • Wei Lan & Chih-Ling Tsai, 2026. "Machine Learning and Covariance Matrices," Springer Books, in: Covariance Analysis and Beyond, chapter 0, pages 139-182, Springer.
    • Handle: RePEc:spr:sprchp:978-3-032-08796-6_9
    DOI: 10.1007/978-3-032-08796-6_9
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