Recent technology and equipment advancements provide with us opportunities to better analyze Alzheimer's disease (AD), where we could collect and employ the data from different image and genetic modalities that may potentially enhance the predictive performance. To perform better clustering in AD analysis, in this paper we propose a novel model to leverage data from all different modalities/views, which can learn the weights of each view adaptively. Different from previous vanilla Non-negative Matrix Factorization which assumes data is linearly separable, we propose a simple yet efficient method based on kernel matrix factorization, which is not only able to deal with non-linear data structure but also can achieve better prediction accuracy. Experimental results on ADNI dataset demonstrate the effectiveness of our proposed method, which indicate promising prospects of kernel application in AD analysis.

本文提出了一种基于核矩阵分解的新型模型，可以更好地分析阿尔茨海默病，处理来自不同视图和遗传模式的数据，并提高预测性能，实验证明了该方法在ADNI数据集上的有效性和核应用于AD分析的前景。

自适应加权多视图核矩阵分解及其在阿尔茨海默病分析中的应用 --聚类角度