ACM Transactions on Intelligent Systems and Technology (TIST) - Special Issue on Crowd in Intelligent Systems, Research Note/Short Paper and Regular Papers: Volume 7 Issue 4, July 2016
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Generalized fused lasso (GFL) penalizes variables with l 1 norms 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 do not scale ...
Alzheimer’s disease, parametric cut, Generalized fused lasso, background subtraction
AAAI'15: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence
Publisher: AAAI Press
Neuroimage analysis usually involves learning thousands or even millions of variables using only a limited number of samples. In this regard, sparse models, e.g. the lasso, are applied to select the optimal features and achieve high diagnosis accuracy. The lasso, however, usually results in independent unstable features. Stability, a manifest ...