A novel nonlinear metric learning method is proposed to improve patient classifications of AD/MCI vs. normal controls. Thin-plate splines are integrated with SVM classifiers to make data points more (linearly) separable. Cross-sectional and longitudinal neuroimaging features estimated from MR brain images are fused through stacked denoising sparse auto-encoder. The effectiveness of the proposed feature transformation and fusion strategies is evaluated with ADNI dataset.