Weighted graph regularized sparse brain network construction for MCI identification
نویسندگان
چکیده
منابع مشابه
Integrating Multiple Network Properties for MCI Identification
Recently, machine learning techniques have been actively applied to the identification of Alzheimer’s disease (AD) and mild cognitive impairment (MCI). However, most of the existing methods focus on using only single network property, although combination of multiple network properties such as local connectivity and topological properties may be more powerful. Employing the kernel-based method,...
متن کاملBrain Connectivity Hyper-Network for MCI Classification
Brain connectivity network has been used for diagnosis and classification of neurodegenerative diseases, such as Alzheimer's disease (AD) as well as its early stage, i.e., mild cognitive impairment (MCI). However, conventional connectivity network is usually constructed based on the pairwise correlation among brain regions and thus ignores the higher-order relationship among them. Such informat...
متن کاملSubspace Clustering via Graph Regularized Sparse Coding
Sparse coding has gained popularity and interest due to the benefits of dealing with sparse data, mainly space and time efficiencies. It presents itself as an optimization problem with penalties to ensure sparsity. While this approach has been studied in the literature, it has rarely been explored within the confines of clustering data. It is our belief that graph-regularized sparse coding can ...
متن کاملL0-norm Sparse Graph-regularized SVD for Biclustering
Learning the “blocking” structure is a central challenge for high dimensional data (e.g., gene expression data). In [Lee et al., 2010], a sparse singular value decomposition (SVD) has been used as a biclustering tool to achieve this goal. However, this model ignores the structural information between variables (e.g., gene interaction graph). Although typical graph-regularized norm can incorpora...
متن کاملSpace-time Adaptive Processing Based on Weighted Regularized Sparse Recovery
In this paper, novel space-time adaptive processing algorithms based on sparse recovery (SR-STAP) that utilize weighted l1-norm penalty are proposed to further enforce the sparsity and approximate the original l0-norm. Because the amplitudes of the clutter components from different snapshots are random variables, we design the corresponding weights according to two different ways, i.e., the Cap...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Pattern Recognition
سال: 2019
ISSN: 0031-3203
DOI: 10.1016/j.patcog.2019.01.015