Supplementary Documents

Supplementary Documents#

In this section, we provide additional resources and mathematical foundations crucial for understanding and applying pattern recognition techniques. Some of topics are covered:

  1. Eigenvalue Decomposition

    • Understanding the decomposition of matrices into eigenvalues and eigenvectors and its applications in pattern recognition.

  2. Convex Programming

    • Linear Programming: Optimization techniques for linear objective functions subject to linear constraints.

    • Quadratic Programming: Optimization involving quadratic objective functions with linear constraints.

  3. Singular Value Decomposition (SVD)

    • Decomposition of matrices and its use in dimensionality reduction and feature extraction.

  4. Optimization Algorithms

    • Techniques for solving optimization problems, including gradient descent and more advanced methods.

  5. Covariance

  6. twin SVM