Papers › Depth Linear Discriminant Analysis
Depth Linear Discriminant Analysis
Abdullah B. Nassera, Waheed Ali H.M. Ghanem, Abdul-Malik H.Y. Saad, Antar Shaddad Hamed Abdul-Qawy, Sanaa A.A. Ghaleb, Nayef Abdulwahab Mohammed Alduais, Fakhrud Din, Mohamed Ghetas
This study introduces a novel linear discriminant method, called Depth Linear Discriminant Analysis (D-LDA) to enhance the robustness of the original LDA. D-LDA systematically integrates the matrix depth concept into LDA, offering a systematic approach to address the challenges associated with scatter matrix estimation. As matrix depth measures how central or deep a particular matrix is within a distribution with respect to different directions, it is an efficient tool for computing a robust scatter matrix estimator that can handle outliers and complex data structures. The experimental results showed that the capacity of D-LDA to reduce data dimensionality and increase class separation yields highly competitive results compared to other LDAs.
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