Alzheimer’s disease (AD) is a chronic progressive neurodegenerative disease that often occurs in the elderly. Electroencephalography (EEG) signals have a strong correlation with neuropsychological test results and brain structural changes. It has become an effective aid in the early diagnosis of AD by exploiting abnormal brain activity. Because the original EEG has the characteristics of weak amplitude, strong background noise and randomness, the research on intelligent AD recognition based on machine learning is still in the exploratory stage. This paper proposes the discriminant subspace low-rank representation (DSLRR) algorithm for EEG-based AD and mild cognitive impairment (MCI) recognition. The subspace learning and low-rank representation are flexibly integrated into a feature representation model. On the one hand, based on the low-rank representation, the graph discriminant embedding is introduced to constrain the representation coefficients, so that the robust representation coefficients can preserve the local manifold structure of the EEG data. On the other hand, the least squares regression, principle component analysis, and global graph embedding are introduced into the subspace learning, to make the model more discriminative. The objective function of DSLRR is solved by the inexact augmented Lagrange multiplier method. The experimental results show that the DSLRR algorithm has good classification performance, which is helpful for in-depth research on AD and MCI recognition.

This paper proposes a novel iterative α -expansion move optical flow estimation with space term disturbance to decrease the local error caused by outlier points. At first, the reason for less smoothness of the discrete optical flow estimation is analyzed. And then, the truncated linear function is adopted and inhibits outlier points caused by the noise and the occlusion because its disturbance makes the solution jump out of the local minimum. Given the theory of the graph cut, the property of decreasing further the energy by disturbing the space term is proven. The experiment shows that this approach could reduce outlier points and smooth further the optical flow field than the estimation with fixed space term.