Quasiconformal kernel common locality discriminant analysis with application to breast cancer diagnosis

Dimensionality reduction (DR) is a popular method in recognition and classification in many areas, such as facial and medical imaging. In this paper, we propose a novel supervised DR method namely Quasiconformal Kernel Common Locality Discriminant Analysis (QKCLDA). QKCLDA preserves the local and discriminative relationships of the data. Moreover, it adjusts the kernel structure according to the distribution of the input data and thus possesses a classification advantage over traditional kernel-based methods. In QKCLDA, the parameter of the quasiconformal kernel is automatically calculated through optimizing an objective function of maximizing the class discriminative ability. QKCLDA is employed in breast cancer diagnoses, and some experiments using Wisconsin Diagnostic Breast Cancer (WDBC) and mini-MIAS databases have tested its feasibility and performance in assigning these diagnoses.