Handwriting Recognition using Kernel Discriminant Analysis

  1.  Introduction
        1. Linear Discriminant Analysis
        2. Non-linear Discriminant Analysis with Kernels
        3. Kernel trick and standard Kernel functions
  2. Source code
  3. Digit recognition
        1. UCI's Optdigits Dataset
        2. Classification by KDA
  4. Test application
        1. Analysis
        2. Results
  5. Conclusion
  6. See also
  7. References

Introduction

Linear Discriminant Analysis

Linear discriminant analysis (LDA) is a method used in statistics and machine learning to find a linear combination of features which best characterize or separates two or more classes of objects or events. The resulting combination may be used as a linear classifier, or, more commonly, for dimensionality reduction before later classification.

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