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	Efficient generalized fused lasso and its application to the diagnosis of Alzheimer's disease
	Xin, Bo ; Kawahara, Yoshinobu ; Wang, Yizhou ; Gao, Wen
	2014
英文摘要	Generalized fused lasso (GFL) penalizes variables with L1norms based both on the variables and their pairwise differences. GFL is useful when applied to data where prior information is expressed using a graph over the variables. However, the existing GFL algorithms incur high computational costs and they do not scale to highdimensional problems. In this study, we propose a fast and scalable algorithm for GFL. Based on the fact that fusion penalty is the Lov??sz extension of a cut function, we show that the key building block of the optimization is equivalent to recursively solving parametric graph-cut problems. Thus, we use a parametric flow algorithm to solve GFL in an efficient manner. Runtime comparisons demonstrated a significant speed-up compared with the existing GFL algorithms. By exploiting the scalability of the proposed algorithm, we formulated the diagnosis of Alzheimer's disease as GFL. Our experimental evaluations demonstrated that the diagnosis performance was promising and that the selected critical voxels were well structured i.e., connected, consistent according to cross-validation and in agreement with prior clinical knowledge. Copyright ? 2014, Association for the Advancement of Artificial Intelligence.; EI; 0
语种	英语
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/329952]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Xin, Bo,Kawahara, Yoshinobu,Wang, Yizhou,et al. Efficient generalized fused lasso and its application to the diagnosis of Alzheimer's disease. 2014-01-01.

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