The timely and accurate diagnosis of Alzheimer's Disease (AD) is important for preventing the progress of the irreversible disease. Recently, various types of imaging techniques, e.g. Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET), have been widely used for the diagnosis of AD. Despite their usefulness, the image data generated are high-dimensional and noisy which make it difficult to give accurate diagnosis. In this paper, we propose a novel feature selection approach to detect the informative features from MRI data, which improves both the diagnosis accuracy and reduces the computational cost. Benchmarking results on the AD challenge datasets show that our proposed approach outperforms other popular feature selection methods. Furthermore, compared with the top winners of the challenge, our approach performs best in diagnosis of AD and has comparable performance with others in the detection of mild cognitive impairments (MCIs). The good performance on the real data demonstrate that our proposed approach is effective in prediction of ADs.
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