Home » Eigenvectors and Eigenvalues: The Key to Unlocking the Power of Linear Algebra in Deep Learning

Eigenvectors and Eigenvalues: The Key to Unlocking the Power of Linear Algebra in Deep Learning


Eigenvectors and eigenvalues are indispensable concepts in linear algebra that underpin numerous machine learning and deep learning techniques. In this article, we explore their fascinating properties and dive into advanced applications that showcase their importance in improving the accuracy of deep learning algorithms.

Eigenspace: A Richer Perspective

When we think of eigenvectors and eigenvalues, we should consider the eigenspace. Eigenspace is a subspace formed by all the eigenvectors associated with a specific eigenvalue, along with the zero vector. For a given matrix \textbf{A} and an eigenvalue \lambda, the eigenspace is defined as:

    \[\textbf{E}_\lambda = { \textbf{v} \in \mathbb{R}^n : \textbf{A}\textbf{v} = \lambda\textbf{v} }\]

Eigenspaces provide a richer perspective on the transformative properties of matrices and reveal the underlying geometric structure of the data. This insight is valuable in deep learning, where algorithms seek to extract meaningful representations and patterns from high-dimensional data.

Eigen Definitions

An eigenvector (\textbf{v}) is a non-zero vector that, when multiplied by a matrix (\textbf{A}), returns a scalar multiple of itself. The scalar multiple is known as the eigenvalue (\lambda), which can be formally defined as:

    \[\textbf{A}\textbf{v} = \lambda\textbf{v}\]

Eigens in Neural Networks

In a neural network, eigenvectors and eigenvalues can be utilized to transform the input data into a new space that is optimized for deep learning. This process is achieved by performing a matrix operation known as eigendecomposition. It breaks down a matrix (\textbf{A}) into its constituent eigenvectors (\textbf{V}) and eigenvalues (\boldsymbol{\Lambda}), such that:

    \[\textbf{A} = \textbf{V} \boldsymbol{\Lambda} \textbf{V}^{-1}\]

The transformed data can then be used to train a neural network and improve its accuracy over time. One well-known example of this technique is Principal Component Analysis (PCA), a dimensionality reduction method that uses eigenvectors and eigenvalues to find the most significant directions (principal components) in the data.

Advanced Applications

Eigenvectors and eigenvalues have been employed in various advanced deep learning applications and research papers:

  1. Optimal Control of Dynamical Systems [1]: Eigenvectors and eigenvalues are used to solve linear quadratic regulator (LQR) problems, which aim to control dynamical systems in an optimal manner. In reinforcement learning, these methods can be applied to guide the learning process in continuous state and action spaces.
  2. Spectral Graph Theory [2]: Spectral graph theory explores the connections between a graph’s structure and the eigenvectors and eigenvalues of its adjacency or Laplacian matrix. This field is relevant to deep learning when dealing with graph-structured data, as in Graph Neural Networks (GNNs) and Graph Convolutional Networks (GCNs).
  3. Diffusion Maps [3]: Diffusion maps is a non-linear dimensionality reduction technique that leverages eigenvectors and eigenvalues to embed high-dimensional data points into a lower-dimensional space. This method is useful in deep learning for unsupervised learning tasks, such as clustering and manifold learning.
  4. Iterative Methods for Large-Scale Eigenvalue Problems [4]: In the context of deep learning, solving large-scale eigenvalue problems becomes crucial for tasks like understanding the Hessian matrix or optimizing large models. Algorithms like the Lanczos method and the Arnoldi iteration employ eigenvectors and eigenvalues to address these challenges.
  5. Understanding and Improving Neural Network Training Dynamics [5]: Eigenvectors and eigenvalues can be used to analyze the dynamics of deep neural network training. For instance, studying the spectrum of the Hessian matrix or Fisher information matrix can shed light on the convergence properties and generalization capabilities of a model.


Eigenvectors and eigenvalues offer a wealth of insights and applications in deep learning, from understanding the geometry of data to advanced optimization techniques. By appreciating the powerful and elegant properties of eigenvectors and eigenvalues, we can harness their full potential to develop sophisticated algorithms that make accurate predictions and solve complex problems.

For More Information

Here are some resources for learning about eigenvectors and eigenvalues in deep learning:

  1. Eigenvectors and Eigenvalues Explained Visually – An interactive website that provides a visual and intuitive explanation of eigenvectors and eigenvalues.
  2. Linear Algebra for Deep Learning – A comprehensive article that covers eigenvectors and eigenvalues, as well as other linear algebra concepts and their application in deep learning.
  3. Deep Learning Book – A comprehensive textbook on deep learning that includes a thorough explanation of eigenvectors and eigenvalues.
  4. CS229: Machine Learning – Stanford University’s course on machine learning that covers eigenvectors and eigenvalues in depth.
  5. Essence of Linear Algebra – A video series by 3Blue1Brown that provides a visual and intuitive understanding of eigenvectors and eigenvalues, as well as other linear algebra concepts.

These resources and other articles on nnlabs.org should provide a solid foundation for understanding eigenvectors and eigenvalues in deep learning.


[1] D. P. Bertsekas, “Dynamic Programming and Optimal Control,” Athena Scientific, 2017.

[2] F. R. K. Chung, “Spectral Graph Theory,” American Mathematical Society, 1997.

[3] R. R. Coifman and S. Lafon, “Diffusion Maps,” Applied and Computational Harmonic Analysis, vol. 21, no. 1, pp. 5-30, 2006.

[4] Y. Saad, “Iterative Methods for Sparse Linear Systems,” SIAM, 2003.

[5] J. Pennington, R. Socher, and C. D. Manning, “Spectrum of the Hessian Matrix of a Neural Network,” Advances in Neural Information Processing Systems 31, 2018.

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